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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

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

Task Management Commands

# 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

Begin Implementation: Start with Task 1 (ModelManager) as it has no dependencies
Follow Dependencies: Respect the dependency chain to ensure proper implementation order
Track Progress: Use Taskmaster's progress tracking and update features
Validate Success Criteria: Ensure each task meets its defined success criteria before completion
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

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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