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https://github.com/Monadical-SAS/reflector.git
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docs: docs website + installation (#778)
* feat: WIP doc (vibe started and iterated) * install from scratch docs * caddyfile.example * gitignore * authentik script * authentik script * authentik script * llm doc * authentik ongoing * more daily setup logs * doc website * gpu self hosted setup guide (no-mistakes) * doc review round * doc review round * doc review round * update doc site sidebars * feat(docs): add mermaid diagram support * docs polishing * live pipeline doc * move pipeline dev docs to dev docs location * doc pr review iteration * dockerfile healthcheck * docs/pr-comments * remove jwt comment * llm suggestion * pr comments * pr comments * document auto migrations * cleanup docs --------- Co-authored-by: Mathieu Virbel <mat@meltingrocks.com> Co-authored-by: Igor Loskutov <igor.loskutoff@gmail.com>
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docs/docs/concepts/pipeline.md
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docs/docs/concepts/pipeline.md
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---
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sidebar_position: 4
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title: Processing Pipeline
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---
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# Processing Pipeline
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Reflector uses a modular pipeline architecture to process audio efficiently and accurately.
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## Pipeline Overview
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The processing pipeline consists of modular components that can be combined and configured based on your needs:
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```mermaid
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graph LR
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A[Audio Input] --> B[Pre-processing]
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B --> C[Chunking]
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C --> D[Transcription]
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D --> E[Diarization]
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E --> F[Alignment]
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F --> G[Post-processing]
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G --> H[Output]
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```
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## Pipeline Components
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### Audio Input
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Accepts various input sources:
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- **File Upload**: MP3, WAV, M4A, WebM, MP4
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- **WebRTC Stream**: Live browser audio
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- **Recording Integration**: Daily.co and Whereby recordings
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- **API Upload**: Direct API submission
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### Pre-processing
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Prepares audio for optimal processing:
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- **Format Conversion**: Convert to 16kHz mono WAV
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- **Noise Reduction**: Optional background noise removal
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- **Validation**: Check duration and quality
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### Chunking
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Splits audio for parallel processing:
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- **Configurable Size**: Audio split into processable segments
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- **Silence Detection**: Optional splitting at natural pauses
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- **Metadata**: Track chunk positions
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### Transcription
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Converts speech to text:
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- **Model Selection**: Whisper or Parakeet
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- **Language Detection**: Automatic or specified
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- **Timestamp Generation**: Word-level timing
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- **Confidence Scores**: Quality indicators
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### Diarization
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Identifies different speakers:
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- **Voice Activity Detection**: Find speech segments
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- **Speaker Embedding**: Extract voice characteristics
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- **Clustering**: Group similar voices
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- **Label Assignment**: Assign speaker IDs
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### Alignment
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Merges all processing results:
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- **Chunk Assembly**: Combine transcription chunks
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- **Speaker Mapping**: Align speakers with text
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- **Overlap Resolution**: Handle chunk boundaries
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- **Timeline Creation**: Build unified timeline
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### Post-processing
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Enhances the final output:
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- **Formatting**: Apply punctuation and capitalization
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- **Summarization**: Generate concise summaries
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- **Topic Extraction**: Identify key themes
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- **Action Items**: Extract tasks and decisions
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## Processing Modes
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### Batch Processing
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For uploaded files:
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- Optimized for throughput
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- Parallel chunk processing
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- Higher accuracy models
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- Complete file analysis
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### Stream Processing
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For live audio:
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- Optimized for latency
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- Sequential processing
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- Real-time feedback
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- Progressive results
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### Hybrid Processing
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For meetings:
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- Stream during meeting
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- Batch after completion
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- Best of both modes
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- Maximum accuracy
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## Pipeline Orchestration
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### Error Handling
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Error recovery:
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- **Automatic Retry**: Failed tasks retry up to 3 times
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- **Partial Recovery**: Continue with successful chunks
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- **Fallback Models**: Use alternative models on failure
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- **Error Reporting**: Detailed error messages
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### Progress Tracking
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Real-time progress updates:
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- **Chunk Progress**: Track individual chunk processing
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- **Overall Progress**: Percentage completion
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- **ETA Calculation**: Estimated completion time
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- **WebSocket Updates**: Live progress to clients
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## Optimization Strategies
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### GPU Utilization
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Maximize GPU efficiency:
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- **Batch Processing**: Process multiple chunks together
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- **Model Caching**: Keep models loaded in memory
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- **Dynamic Batching**: Adjust batch size based on GPU memory
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- **Multi-GPU Support**: Distribute across available GPUs
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### Memory Management
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Efficient memory usage:
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- **Streaming Processing**: Process large files in chunks
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- **Garbage Collection**: Clean up after each chunk
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- **Memory Limits**: Prevent out-of-memory errors
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- **Disk Caching**: Use disk for large intermediate results
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### Network Optimization
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Minimize network overhead:
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- **Compression**: Compress audio before transfer
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- **CDN Integration**: Use CDN for static assets
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- **Connection Pooling**: Reuse network connections
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- **Parallel Uploads**: Multiple concurrent uploads
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## Quality Assurance
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### Accuracy Metrics
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Monitor processing quality:
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- **Word Error Rate (WER)**: Transcription accuracy
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- **Diarization Error Rate (DER)**: Speaker identification accuracy
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- **Summary Coherence**: Summary quality metrics
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### Validation Steps
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Ensure output quality:
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- **Confidence Thresholds**: Filter low-confidence segments
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- **Consistency Checks**: Verify timeline consistency
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- **Language Validation**: Ensure correct language detection
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- **Format Validation**: Check output format compliance
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## Advanced Features
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### Custom Models
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Use your own models:
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- **Fine-tuned Whisper**: Domain-specific models
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- **Custom Diarization**: Trained on your speakers
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- **Specialized Post-processing**: Industry-specific formatting
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### Pipeline Extensions
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Add custom processing steps:
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- **Sentiment Analysis**: Analyze emotional tone
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- **Entity Extraction**: Identify people, places, organizations
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- **Custom Metrics**: Calculate domain-specific metrics
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- **Integration Hooks**: Call external services
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