# Trax v2 Taskmaster Implementation Summary

## 🎯 Overview

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.

## 📊 Task Statistics

- **Total Tasks**: 7 main tasks
- **Total Subtasks**: 35 subtasks (all tasks expanded)
- **Priority Distribution**: 4 high priority, 3 medium priority
- **Dependencies**: Well-structured dependency chain starting with foundational components

## 🏗️ Task Architecture

### Phase 1: Core Multi-Pass Pipeline Foundation (Tasks 1, 6, 7)

**Goal**: Implement the foundation multi-pass transcription pipeline with enhanced task system, ModelManager singleton, and basic multi-pass pipeline.

#### Task 1: ModelManager Singleton Implementation
- **Priority**: High
- **Dependencies**: None (foundational)
- **Subtasks**:
  1.1. Implement Singleton Pattern and Model Configuration
  1.2. Implement Model Loading and Quantization
  1.3. Implement Memory Management Functions
  1.4. Implement Thread Safety for Concurrent Access
  1.5. Implement Model Caching and Performance Optimization

#### Task 6: Database Schema Migration for v2
- **Priority**: High
- **Dependencies**: 1, 2, 3
- **Subtasks**:
  6.1. Create new tables for speaker profiles and processing jobs
  6.2. Add v2 columns to existing transcripts table
  6.3. Create Alembic migration scripts
  6.4. Implement SQLAlchemy models for new schema
  6.5. Implement data migration and backward compatibility

#### Task 7: Multi-Pass Transcription Pipeline Implementation
- **Priority**: High
- **Dependencies**: 1, 2, 3
- **Subtasks**:
  7.1. Implement First Pass Transcription Module
  7.2. Implement Confidence Calculation System
  7.3. Implement Refinement Pass Module
  7.4. Implement AI Enhancement Pass with Domain Adaptation
  7.5. Implement Result Merging and Parallel Processing

### Phase 2: Speaker Diarization Integration (Task 2)

**Goal**: Integrate Pyannote.audio for speaker identification with parallel processing and speaker profiles.

#### Task 2: Speaker Diarization with Pyannote.audio
- **Priority**: High
- **Dependencies**: 1
- **Subtasks**:
  2.1. Implement Pyannote.audio Integration
  2.2. Implement Parallel Processing for Diarization and Transcription
  2.3. Develop Speaker Profile Management System
  2.4. Implement Diarization-Transcript Merging Algorithm
  2.5. Implement Configuration and Memory Optimization

### Phase 3: Domain Adaptation and LoRA (Task 3)

**Goal**: Implement domain-specific model adaptation using LoRA adapters for technical, medical, and academic domains.

#### Task 3: Domain Adaptation System with LoRA Adapters
- **Priority**: Medium
- **Dependencies**: 1
- **Subtasks**:
  3.1. Implement LoRA Adapter Architecture
  3.2. Implement Domain Detection System
  3.3. Integrate Domain Adaptation with Model Manager
  3.4. Implement Memory Optimization for Adapters
  3.5. Implement Performance Optimizations for Domain Switching

### Phase 4: Enhanced CLI Interface (Task 4)

**Goal**: Develop enhanced CLI interface with improved batch processing, progress reporting, and performance monitoring.

#### Task 4: Enhanced CLI Interface with Progress Reporting
- **Priority**: Medium
- **Dependencies**: 1, 2, 3
- **Subtasks**:
  4.1. Implement Command-line Interface Structure
  4.2. Develop Batch Processing with Intelligent Queuing
  4.3. Implement Real-time Progress Reporting
  4.4. Add Performance Monitoring and Error Handling
  4.5. Implement Export Functionality with Multiple Formats

### Phase 5: Performance Optimization and Polish (Task 5)

**Goal**: Achieve performance targets and final polish through comprehensive performance benchmarking, memory optimization, processing optimization, and final testing.

#### Task 5: Comprehensive Performance Benchmarking and Optimization
- **Priority**: Medium
- **Dependencies**: 1, 2, 3, 4
- **Subtasks**:
  5.1. Implement Performance Profiling Infrastructure
  5.2. Develop Visualization and Reporting System
  5.3. Implement Memory Optimization Strategies
  5.4. Implement Processing Speed Optimizations
  5.5. Create Interactive Optimization Dashboard

## 🎯 Success Criteria Alignment

### Performance Targets
- **Processing Speed**: <25 seconds for 5-minute audio
- **Accuracy**: 99.5%+ transcription accuracy
- **Memory Usage**: <8GB peak usage
- **Speaker Diarization**: 90%+ speaker identification accuracy
- **Domain Adaptation**: 2%+ improvement per domain

### Technical Requirements
- **Multi-Pass Pipeline**: Fast pass (distil-small.en) + refinement pass (distil-large-v3) + enhancement
- **Parallel Processing**: Concurrent transcription and diarization
- **Model Caching**: Singleton ModelManager with 8-bit quantization
- **Database Schema**: Enhanced with speaker profiles and processing jobs
- **CLI Interface**: Real-time progress reporting and batch processing

## 🔄 Implementation Workflow

### Recommended Development Order
1. **Start with Task 1** (ModelManager) - foundational component with no dependencies
2. **Proceed to Task 6** (Database Schema) - required for all v2 features
3. **Implement Task 7** (Multi-Pass Pipeline) - core v2 functionality
4. **Add Task 2** (Speaker Diarization) - parallel processing capability
5. **Expand remaining tasks** as dependencies are satisfied

### Task Management Commands
```bash
# View current task status
task-master list --with-subtasks

# Get next recommended task
task-master next

# Start working on a task
task-master set-status --id=1 --status=in-progress

# View detailed task information
task-master show 1

# Update task progress
task-master update-subtask --id=1.1 --prompt "Completed singleton pattern implementation"
```

## 📋 Key Implementation Details

### ModelManager Singleton (Task 1)
- **Purpose**: Central model management to prevent memory duplication
- **Features**: Model caching, 8-bit quantization, memory management
- **Models**: distil-small.en (fast pass), distil-large-v3 (refinement)
- **Memory**: <8GB peak usage with quantization

### Multi-Pass Pipeline (Task 7)
- **Stage 1**: Fast pass with distil-small.en (10-15 seconds)
- **Stage 2**: Confidence scoring and low-confidence segment identification
- **Stage 3**: Refinement pass with distil-large-v3 for accuracy improvement
- **Stage 4**: AI enhancement using DeepSeek (optional)
- **Target**: 99.5%+ accuracy, <25 seconds processing time

### Speaker Diarization (Task 2)
- **Technology**: Pyannote.audio integration
- **Features**: Parallel processing, speaker profiles, embedding caching
- **Accuracy**: 90%+ speaker identification
- **Integration**: Merged with transcript timestamps

### Domain Adaptation (Task 3)
- **Technology**: LoRA adapters for lightweight domain-specific models
- **Domains**: Technical, medical, academic, general
- **Features**: Domain auto-detection, fast adapter switching, memory optimization
- **Target**: 2%+ accuracy improvement per domain

### Enhanced CLI Interface (Task 4)
- **Features**: Real-time progress reporting, batch processing, performance monitoring
- **Batch Processing**: Intelligent queuing, configurable concurrency
- **Export Formats**: JSON, TXT, SRT, DOCX with speaker labels
- **Error Handling**: Clear retry guidance and recovery suggestions

### Performance Optimization (Task 5)
- **Profiling**: Comprehensive performance benchmarking infrastructure
- **Visualization**: Interactive dashboard for performance metrics
- **Memory Optimization**: Advanced memory management strategies
- **Speed Optimization**: Pipeline stage and parallel processing improvements

### Database Schema (Task 6)
- **New Tables**: speaker_profiles, processing_jobs
- **Enhanced Columns**: pipeline_version, enhanced_content, diarization_content, merged_content, model_used, domain_used, accuracy_estimate, confidence_scores, speaker_count
- **Migration**: Alembic-based with backward compatibility

## 🚀 Next Steps

1. **Begin Implementation**: Start with Task 1 (ModelManager) as it has no dependencies
2. **Follow Dependencies**: Respect the dependency chain to ensure proper implementation order
3. **Track Progress**: Use Taskmaster's progress tracking and update features
4. **Validate Success Criteria**: Ensure each task meets its defined success criteria before completion
5. **Iterative Development**: Use the subtask structure for incremental development and testing

## 📚 Documentation References

- **Architecture Document**: `.taskmaster/docs/trax-v2-architecture.md`
- **Implementation Plan**: `.taskmaster/docs/trax-v2-implementation-plan.md`
- **Product Requirements**: `.taskmaster/docs/prd-v2.0.md`
- **Taskmaster Configuration**: `.taskmaster/config.json`

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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.*
