211 lines
8.9 KiB
Markdown
211 lines
8.9 KiB
Markdown
# Trax v2 Taskmaster Implementation Summary
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## 🎯 Overview
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This document summarizes the comprehensive Taskmaster task set created for Trax v2 implementation, based on the architecture document, implementation plan, and PRD. The tasks are organized in the `trax-v2` tag and follow a structured 5-phase approach over 10 weeks.
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## 📊 Task Statistics
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- **Total Tasks**: 7 main tasks
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- **Total Subtasks**: 35 subtasks (all tasks expanded)
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- **Priority Distribution**: 4 high priority, 3 medium priority
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- **Dependencies**: Well-structured dependency chain starting with foundational components
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## 🏗️ Task Architecture
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### Phase 1: Core Multi-Pass Pipeline Foundation (Tasks 1, 6, 7)
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**Goal**: Implement the foundation multi-pass transcription pipeline with enhanced task system, ModelManager singleton, and basic multi-pass pipeline.
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#### Task 1: ModelManager Singleton Implementation
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- **Priority**: High
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- **Dependencies**: None (foundational)
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- **Subtasks**:
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1.1. Implement Singleton Pattern and Model Configuration
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1.2. Implement Model Loading and Quantization
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1.3. Implement Memory Management Functions
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1.4. Implement Thread Safety for Concurrent Access
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1.5. Implement Model Caching and Performance Optimization
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#### Task 6: Database Schema Migration for v2
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- **Priority**: High
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- **Dependencies**: 1, 2, 3
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- **Subtasks**:
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6.1. Create new tables for speaker profiles and processing jobs
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6.2. Add v2 columns to existing transcripts table
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6.3. Create Alembic migration scripts
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6.4. Implement SQLAlchemy models for new schema
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6.5. Implement data migration and backward compatibility
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#### Task 7: Multi-Pass Transcription Pipeline Implementation
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- **Priority**: High
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- **Dependencies**: 1, 2, 3
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- **Subtasks**:
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7.1. Implement First Pass Transcription Module
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7.2. Implement Confidence Calculation System
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7.3. Implement Refinement Pass Module
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7.4. Implement AI Enhancement Pass with Domain Adaptation
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7.5. Implement Result Merging and Parallel Processing
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### Phase 2: Speaker Diarization Integration (Task 2)
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**Goal**: Integrate Pyannote.audio for speaker identification with parallel processing and speaker profiles.
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#### Task 2: Speaker Diarization with Pyannote.audio
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- **Priority**: High
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- **Dependencies**: 1
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- **Subtasks**:
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2.1. Implement Pyannote.audio Integration
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2.2. Implement Parallel Processing for Diarization and Transcription
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2.3. Develop Speaker Profile Management System
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2.4. Implement Diarization-Transcript Merging Algorithm
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2.5. Implement Configuration and Memory Optimization
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### Phase 3: Domain Adaptation and LoRA (Task 3)
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**Goal**: Implement domain-specific model adaptation using LoRA adapters for technical, medical, and academic domains.
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#### Task 3: Domain Adaptation System with LoRA Adapters
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- **Priority**: Medium
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- **Dependencies**: 1
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- **Subtasks**:
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3.1. Implement LoRA Adapter Architecture
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3.2. Implement Domain Detection System
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3.3. Integrate Domain Adaptation with Model Manager
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3.4. Implement Memory Optimization for Adapters
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3.5. Implement Performance Optimizations for Domain Switching
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### Phase 4: Enhanced CLI Interface (Task 4)
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**Goal**: Develop enhanced CLI interface with improved batch processing, progress reporting, and performance monitoring.
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#### Task 4: Enhanced CLI Interface with Progress Reporting
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- **Priority**: Medium
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- **Dependencies**: 1, 2, 3
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- **Subtasks**:
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4.1. Implement Command-line Interface Structure
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4.2. Develop Batch Processing with Intelligent Queuing
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4.3. Implement Real-time Progress Reporting
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4.4. Add Performance Monitoring and Error Handling
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4.5. Implement Export Functionality with Multiple Formats
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### Phase 5: Performance Optimization and Polish (Task 5)
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**Goal**: Achieve performance targets and final polish through comprehensive performance benchmarking, memory optimization, processing optimization, and final testing.
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#### Task 5: Comprehensive Performance Benchmarking and Optimization
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- **Priority**: Medium
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- **Dependencies**: 1, 2, 3, 4
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- **Subtasks**:
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5.1. Implement Performance Profiling Infrastructure
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5.2. Develop Visualization and Reporting System
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5.3. Implement Memory Optimization Strategies
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5.4. Implement Processing Speed Optimizations
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5.5. Create Interactive Optimization Dashboard
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## 🎯 Success Criteria Alignment
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### Performance Targets
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- **Processing Speed**: <25 seconds for 5-minute audio
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- **Accuracy**: 99.5%+ transcription accuracy
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- **Memory Usage**: <8GB peak usage
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- **Speaker Diarization**: 90%+ speaker identification accuracy
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- **Domain Adaptation**: 2%+ improvement per domain
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### Technical Requirements
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- **Multi-Pass Pipeline**: Fast pass (distil-small.en) + refinement pass (distil-large-v3) + enhancement
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- **Parallel Processing**: Concurrent transcription and diarization
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- **Model Caching**: Singleton ModelManager with 8-bit quantization
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- **Database Schema**: Enhanced with speaker profiles and processing jobs
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- **CLI Interface**: Real-time progress reporting and batch processing
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## 🔄 Implementation Workflow
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### Recommended Development Order
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1. **Start with Task 1** (ModelManager) - foundational component with no dependencies
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2. **Proceed to Task 6** (Database Schema) - required for all v2 features
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3. **Implement Task 7** (Multi-Pass Pipeline) - core v2 functionality
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4. **Add Task 2** (Speaker Diarization) - parallel processing capability
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5. **Expand remaining tasks** as dependencies are satisfied
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### Task Management Commands
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```bash
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# View current task status
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task-master list --with-subtasks
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# Get next recommended task
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task-master next
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# Start working on a task
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task-master set-status --id=1 --status=in-progress
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# View detailed task information
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task-master show 1
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# Update task progress
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task-master update-subtask --id=1.1 --prompt "Completed singleton pattern implementation"
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```
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## 📋 Key Implementation Details
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### ModelManager Singleton (Task 1)
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- **Purpose**: Central model management to prevent memory duplication
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- **Features**: Model caching, 8-bit quantization, memory management
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- **Models**: distil-small.en (fast pass), distil-large-v3 (refinement)
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- **Memory**: <8GB peak usage with quantization
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### Multi-Pass Pipeline (Task 7)
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- **Stage 1**: Fast pass with distil-small.en (10-15 seconds)
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- **Stage 2**: Confidence scoring and low-confidence segment identification
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- **Stage 3**: Refinement pass with distil-large-v3 for accuracy improvement
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- **Stage 4**: AI enhancement using DeepSeek (optional)
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- **Target**: 99.5%+ accuracy, <25 seconds processing time
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### Speaker Diarization (Task 2)
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- **Technology**: Pyannote.audio integration
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- **Features**: Parallel processing, speaker profiles, embedding caching
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- **Accuracy**: 90%+ speaker identification
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- **Integration**: Merged with transcript timestamps
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### Domain Adaptation (Task 3)
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- **Technology**: LoRA adapters for lightweight domain-specific models
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- **Domains**: Technical, medical, academic, general
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- **Features**: Domain auto-detection, fast adapter switching, memory optimization
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- **Target**: 2%+ accuracy improvement per domain
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### Enhanced CLI Interface (Task 4)
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- **Features**: Real-time progress reporting, batch processing, performance monitoring
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- **Batch Processing**: Intelligent queuing, configurable concurrency
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- **Export Formats**: JSON, TXT, SRT, DOCX with speaker labels
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- **Error Handling**: Clear retry guidance and recovery suggestions
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### Performance Optimization (Task 5)
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- **Profiling**: Comprehensive performance benchmarking infrastructure
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- **Visualization**: Interactive dashboard for performance metrics
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- **Memory Optimization**: Advanced memory management strategies
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- **Speed Optimization**: Pipeline stage and parallel processing improvements
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### Database Schema (Task 6)
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- **New Tables**: speaker_profiles, processing_jobs
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- **Enhanced Columns**: pipeline_version, enhanced_content, diarization_content, merged_content, model_used, domain_used, accuracy_estimate, confidence_scores, speaker_count
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- **Migration**: Alembic-based with backward compatibility
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## 🚀 Next Steps
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1. **Begin Implementation**: Start with Task 1 (ModelManager) as it has no dependencies
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2. **Follow Dependencies**: Respect the dependency chain to ensure proper implementation order
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3. **Track Progress**: Use Taskmaster's progress tracking and update features
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4. **Validate Success Criteria**: Ensure each task meets its defined success criteria before completion
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5. **Iterative Development**: Use the subtask structure for incremental development and testing
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## 📚 Documentation References
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- **Architecture Document**: `.taskmaster/docs/trax-v2-architecture.md`
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- **Implementation Plan**: `.taskmaster/docs/trax-v2-implementation-plan.md`
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- **Product Requirements**: `.taskmaster/docs/prd-v2.0.md`
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- **Taskmaster Configuration**: `.taskmaster/config.json`
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---
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*This summary provides a comprehensive overview of the Trax v2 implementation tasks created in Taskmaster. The complete task set includes 7 main tasks and 35 detailed subtasks, following the 5-phase implementation plan and designed to achieve the high-performance, speaker diarization, and domain adaptation goals outlined in the PRD.*
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