Comprehensive Analysis Pipeline

Comprehensive Analysis Pipeline

Overview

DeepTalk implements a sophisticated 7-stage comprehensive analysis pipeline that processes audio/video files into structured, analyzed, and searchable transcripts. The pipeline combines multiple AI technologies with robust engineering practices to deliver reliable, feature-rich transcript analysis capabilities.

Pipeline Architecture

┌─────────────────────────────────────────────────────────────────────────────┐
│                          7-Stage Processing Pipeline                        │
├─────────────────────────────────────────────────────────────────────────────┤
│ Stage 1: Media Analysis (0-20%)    → Audio Extraction                      │
│ Stage 2: Transcription (20-65%)    → Speech-to-Text Processing             │
│ Stage 3: Validation (65-70%)       → AI-Powered Correction                 │
│ Stage 4: Basic Analysis (70-75%)   → Summary, Topics, Actions              │
│ Stage 5: Advanced Analysis (75-85%) → Sentiment, Emotion, Speakers         │
│ Stage 6: Research Analysis (85-95%) → Quotes, Themes, Q&A, Concepts        │
│ Stage 7: Persistence (95-100%)     → Database Storage & Segmentation       │
└─────────────────────────────────────────────────────────────────────────────┘

Multi-Stage Processing Architecture

Stage 1: Media Analysis and Audio Extraction

Location: /src/services/fileProcessor.ts (lines 19-45)
Progress: 0-20% (“analyzing”, “extracting”)

Process:

  • Analyzes media files to detect video/audio content
  • Extracts audio from video files using FFmpeg when needed
  • Creates temporary audio files for processing
  • Validates file format and accessibility

Key Features:

  • Format Detection: Automatic video/audio content identification
  • FFmpeg Integration: Robust audio extraction from video files
  • Temporary File Management: Clean handling of intermediate files
  • Error Handling: Graceful failure for unsupported formats

Stage 2: Speech-to-Text Transcription

Location: /src/services/fileProcessor.ts (lines 47-62, 200-242)
Progress: 20-65% (“transcribing”)

Process:

  • Configurable STT service integration (default: Speaches service)
  • Chunk-based transcription with precise timing information
  • Returns full text with chunk timings for sentence segmentation
  • Handles various audio qualities and formats

Key Features:

  • Service Abstraction: Pluggable STT service architecture
  • Timing Precision: Chunk-level timestamp preservation
  • Quality Handling: Adaptive processing for different audio qualities
  • Progress Tracking: Real-time transcription progress updates

Stage 3: Transcript Validation and Correction

Location: /src/services/fileProcessor.ts (lines 64-67, 969-1350)
Progress: 65-70% (“validating”)

Process:

  • AI-powered validation for spelling, grammar, punctuation, capitalization
  • Duplicate sentence removal with similarity analysis
  • Chunked validation for large transcripts (4000+ characters)
  • Change tracking with position information
  • Fallback mechanisms for processing failures

Validation Options:

  • Spelling corrections: Fix transcription errors
  • Grammar improvements: Enhance sentence structure
  • Punctuation fixes: Add missing punctuation
  • Capitalization corrections: Proper noun and sentence capitalization

Key Features:

  • Chunked Processing: Efficient handling of large transcripts
  • Change Tracking: Detailed modification logging
  • Similarity Analysis: Intelligent duplicate detection
  • Configurable Options: User-selectable validation types

Stage 4: Basic AI Analysis

Location: /src/services/fileProcessor.ts (lines 78-83, 244-323)
Progress: 70-75% (“analyzing”)

Process:

  • Configurable prompt-based analysis using AI models
  • Extracts summary, key topics, and action items
  • JSON response parsing with fallback text parsing
  • Standardized analysis format across different models

Analysis Types:

  • Summary Generation: 2-3 sentence concise summaries
  • Key Topics Extraction: Bullet-point topic identification
  • Action Items: Next steps and task identification

Key Features:

  • Prompt Templates: Configurable analysis prompts
  • Multi-Model Support: Works with various AI providers
  • Fallback Parsing: Graceful handling of non-JSON responses
  • Standardized Output: Consistent analysis format

Stage 5: Advanced Analysis

Location: /src/services/fileProcessor.ts (lines 85-92, 775-916)
Progress: 75-85% (“analyzing”)

Process:

  • Sentiment Analysis: Overall sentiment with numerical scoring (-1 to +1)
  • Emotion Analysis: Multi-dimensional emotion detection
  • Speaker Detection: Rule-based + AI hybrid approach
  • Speaker Tagging: Assigns speakers to text segments with pattern analysis

Advanced Analysis Types:

Sentiment Analysis

  • Scoring Range: -1 (very negative) to +1 (very positive)
  • Granular Detection: Positive, negative, neutral classifications
  • Context Awareness: Considers conversation context

Emotion Analysis

  • Multi-Dimensional: 6+ emotion categories (frustration, excitement, etc.)
  • Intensity Scoring: Relative emotion strength measurement
  • Contextual Analysis: Emotion evolution throughout conversation

Speaker Analysis

  • Count Detection: Determine number of distinct speakers
  • Pattern Recognition: Conversation flow analysis
  • Segment Assignment: Speaker attribution for text segments
  • Confidence Scoring: Reliability metrics for speaker identification

Stage 6: Research Analysis

Location: /src/services/fileProcessor.ts (lines 94-97, 1352-1556)
Progress: 85-95% (“analyzing”)

Process:

  • Notable Quotes: Extraction with relevance scoring
  • Research Themes: Thematic analysis with confidence scores
  • Q&A Pairs: Question-answer pattern identification
  • Concept Frequency: Key concept tracking with contextual examples

Research Analysis Types:

Notable Quotes

  • Relevance Scoring: Importance-based quote ranking
  • Context Preservation: Surrounding context for quotes
  • Speaker Attribution: Quote source identification

Research Themes

  • Thematic Analysis: Major theme identification
  • Confidence Scoring: Theme reliability metrics
  • Cross-Reference: Theme interconnection analysis

Q&A Pairs

  • Pattern Recognition: Question-answer sequence identification
  • Context Mapping: Related Q&A grouping
  • Interview Analysis: Structured conversation analysis

Concept Frequency

  • Term Frequency: Key concept occurrence tracking
  • Contextual Examples: Sample usage for each concept
  • Semantic Grouping: Related concept clustering

Stage 7: Data Persistence and Sentence Segmentation

Location: /src/services/fileProcessor.ts (lines 99-184)
Progress: 95-100% (“saving”)

Process:

  • Database storage of all analysis results
  • Sentence-level segmentation with timing estimation
  • Cleanup of temporary files and resources
  • Final error handling and status updates

Key Features:

  • Atomic Operations: Transactional database updates
  • Sentence Segmentation: Intelligent sentence boundary detection
  • Timing Estimation: Sentence-level timestamp calculation
  • Resource Cleanup: Automatic temporary file removal

Progress Tracking System

Progress Callback Architecture

interface ProcessingCallbacks {
  onProgress?: (stage: string, percent: number) => void;
  onError?: (error: Error) => void;
  onComplete?: () => void;
}

Stage Progress Mapping

  • Analyzing: 0-20% (media analysis, audio extraction)
  • Transcribing: 20-65% (STT processing)
  • Validating: 65-70% (correction processing)
  • Analyzing: 70-95% (AI analysis stages)
  • Saving: 95-100% (data persistence)

UI Progress Components

Processing Queue (/src/components/ProcessingQueue.tsx)

  • Real-time Visualization: 0-100% progress bars
  • Status-Specific Colors: Visual feedback for different stages
  • Error Display: Truncated error messages with details
  • Queue Management: Item removal for completed/failed items

Correction Trigger (/src/components/CorrectionTrigger.tsx)

  • Settings-Aware: Correction availability based on configuration
  • Re-correction Warnings: Alerts for existing corrections
  • Progress Feedback: Visual validation progress
  • Error Handling: User-friendly error reporting

Validation and Correction Mechanisms

Multi-Layer Validation System

1. Rule-Based Preprocessing

  • Duplicate Detection: Similarity-based duplicate identification
  • Pattern Analysis: Common transcription error patterns
  • Context Validation: Sentence structure analysis

2. AI-Powered Correction

  • Configurable Options: User-selectable correction types
  • Model Integration: Works with various AI providers
  • Quality Assurance: Validation of correction quality

3. Change Tracking

  • Position Information: Exact change locations
  • Modification Log: Complete audit trail
  • Rollback Capability: Ability to revert changes

Correction Configuration

interface ValidationOptions {
  spelling: boolean;
  grammar: boolean;
  punctuation: boolean;
  capitalization: boolean;
}

Service Integration Architecture

Core Service Dependencies

1. Sentence Segmentation Service

Location: /src/services/sentenceSegmentationService.ts

Features:

  • Smart Boundary Detection: Intelligent sentence splitting
  • Timestamp Estimation: Timing calculation from chunks
  • Confidence Scoring: Reliability metrics for segments
  • Version Management: Original/corrected/speaker_tagged versions

2. Chunking Service

Location: /src/services/chunkingService.ts

Strategies:

  • Speaker-Based: Group by speaker changes
  • Time-Based: Fixed intervals with overlap
  • Hybrid: Speaker + time constraints

3. Prompt Service Integration

  • Template-Based: Configurable AI prompts
  • Variable Substitution: Dynamic content injection
  • Model Compatibility: Prompt optimization per model

Integration Data Flow

File Input → Media Analysis → Transcription → Validation → 
Basic Analysis → Advanced Analysis → Research Analysis → 
Sentence Segmentation → Database Storage → Vector Embedding

Error Handling and Resilience

Pipeline Resilience Strategy

1. Graceful Degradation

  • Stage Independence: Failed stages don’t stop pipeline
  • Partial Results: Save completed analysis even on failure
  • Progress Preservation: Maintain completed work

2. Fallback Mechanisms

  • Text Parsing: JSON parsing failures fall back to text
  • Default Values: Sensible defaults for missing analysis
  • Service Alternatives: Multiple processing paths

3. Error Recovery

  • Retry Logic: Automatic retry for transient failures
  • Error Logging: Comprehensive error tracking
  • User Feedback: Clear error communication

Error Handling Implementation

try {
  const result = await this.performAnalysis(transcript, options);
  await this.saveResults(result);
} catch (error) {
  // Log error but continue pipeline
  this.logError(error);
  return this.getDefaultAnalysis();
}

Performance Optimizations

Processing Efficiency

1. Chunked Processing

  • Large File Handling: Efficient processing of large transcripts
  • Memory Management: Controlled memory usage
  • Progress Granularity: Detailed progress feedback

2. Service Optimization

  • Singleton Pattern: Efficient service instantiation
  • Connection Pooling: Reuse of service connections
  • Caching: Intelligent result caching

3. Resource Management

  • Temporary Files: Automatic cleanup
  • Memory Leaks: Proactive memory management
  • Process Isolation: Service separation for stability

Project-Level Analysis Extension

Project Analysis Service

Location: /src/services/projectAnalysisService.ts

Advanced Analysis Types:

  • Theme Evolution: Track themes across time and transcripts
  • Concept Frequency: Cross-transcript concept analysis
  • Speaker Analysis: Speaker interaction patterns across sessions
  • Timeline Analysis: Temporal trend identification
  • Cross-Transcript Patterns: Pattern detection across multiple sources

Configuration and Customization

Pipeline Configuration

  • Stage Selection: Enable/disable specific analysis stages
  • Model Selection: Choose AI models for different stages
  • Validation Options: Configure correction preferences
  • Progress Callbacks: Custom progress handling

User Interface Integration

  • Settings Pages: Configuration through UI
  • Real-time Feedback: Live progress and status updates
  • Error Reporting: User-friendly error messages
  • Result Visualization: Rich display of analysis results

Key Architectural Strengths

  1. Modular Design: Each stage independently testable and configurable
  2. Robust Error Handling: Multiple fallback mechanisms ensure completion
  3. Progressive Enhancement: Basic functionality works even if advanced features fail
  4. Scalable Processing: Chunked processing handles large files efficiently
  5. Comprehensive Feedback: Detailed progress tracking and error reporting
  6. Flexible Configuration: Extensive customization options
  7. Data Integrity: Version management and change tracking
  8. Performance Optimization: Efficient resource usage and cleanup

Reuse Value for Other Applications

This Comprehensive Analysis Pipeline provides a robust foundation for any application requiring:

  • Multi-stage document processing with progress tracking
  • AI-powered content analysis with multiple analysis types
  • Validation and correction systems with change tracking
  • Scalable file processing with error recovery
  • Configurable analysis pipelines with modular stages
  • Real-time progress feedback with detailed status updates
  • Robust error handling with graceful degradation

The architecture demonstrates enterprise-grade design patterns and can be adapted for various content analysis use cases beyond audio transcription.