AI-Powered Audiobook Generation System

Sophisticated distributed processing system with LLM integration for multi-format audiobook generation

Source Code View Architecture
AI-Powered Audiobook Generation System Interface

About This Project

The AI-Powered Audiobook Generation System is a production-ready distributed processing platform that transforms multi-format documents into professional audiobooks using advanced LLM integration. With enterprise architecture featuring deterministic segmentation, rule-based attribution, and distributed scaling capabilities, it processes ~15 seconds per document with <300ms per chunk performance.

This system features a sophisticated 8,933-line codebase with distributed processing using Apache Kafka, Spark, and Redis, supporting both local (Ollama) and cloud-based LLM engines. The architecture includes Docker containerization, comprehensive monitoring with Prometheus and Grafana, and a complete testing suite for production deployment.

Built with enterprise patterns including dependency injection, distributed pipeline orchestration, and professional code organization, this system demonstrates advanced AI/ML integration with natural language processing, character profiling, and intelligent content filtering for accessibility and educational applications.

Key Features

  • AI-powered LLM integration with local (Ollama) and cloud (GCP Vertex AI) engines
  • Distributed processing architecture with Apache Kafka, Spark, and Redis
  • Deterministic segmentation and rule-based attribution reducing LLM API calls by 50%+
  • Multi-format support (PDF, DOCX, EPUB, MOBI, TXT, MD) with intelligent content filtering
  • Enterprise monitoring with Prometheus, Grafana, and comprehensive health checks
  • Docker containerization with production-ready deployment and scaling
  • Advanced text structuring with contextual refinement and quality validation
  • Professional code organization with dependency injection and testing infrastructure

Technical Challenges

The primary challenge was architecting a distributed processing system that could handle enterprise-scale document processing while maintaining text integrity and preventing corruption. This required developing a deterministic-first approach with rule-based attribution, sophisticated LLM orchestration, and horizontal scaling patterns using Apache Kafka and Spark.

Additional challenges included building a robust monitoring and observability infrastructure, implementing comprehensive testing across distributed components, and designing a professional code organization pattern that could support multiple deployment environments while maintaining performance benchmarks of ~15 seconds per document processing.

Enterprise Processing Pipeline

1

Enhanced Text Extraction

Multi-format parsing with intelligent content filtering, TOC detection, and story content extraction using advanced PDF intelligence

2

AI-Powered Text Structuring

Deterministic-first pipeline with rule-based attribution, LLM orchestration, and distributed processing using Kafka and Spark

3

Contextual Refinement

Conversation flow analysis, quality validation, and character profiling with comprehensive error tracking

4

Distributed Audio Generation

Professional TTS synthesis with character voice assignment, monitoring, and distributed scaling capabilities

5

Production Monitoring

Real-time metrics with Prometheus, Grafana dashboards, health checks, and comprehensive observability

Enterprise Architecture Code Highlights

Distributed Pipeline Orchestrator

class DistributedPipelineOrchestrator:
    def __init__(self, kafka_config, spark_config, redis_config):
        self.kafka_producer = KafkaProducer(**kafka_config)
        self.spark_session = SparkSession.builder.config(spark_config).getOrCreate()
        self.redis_client = redis.Redis(**redis_config)
        self.llm_pool = LLMPoolManager()
    
    async def process_document(self, document_path):
        # Enhanced text extraction with content filtering
        text = await self.extract_with_intelligence(document_path)
        
        # Distributed processing with Kafka events
        await self.kafka_producer.send('text_processing', {
            'document_id': document_path,
            'text': text,
            'timestamp': time.time()
        })

LLM Pool Manager

class LLMPoolManager:
    def __init__(self):
        self.local_pool = [OllamaClient() for _ in range(4)]
        self.cloud_pool = [VertexAIClient() for _ in range(2)]
        self.load_balancer = LoadBalancer()
    
    async def process_chunk(self, chunk, context):
        # Connection pooling and load balancing
        client = await self.load_balancer.get_available_client()
        
        # Deterministic attribution first
        if self.rule_based_attributor.can_handle(chunk):
            return self.rule_based_attributor.process(chunk)
        
        # LLM processing for ambiguous content
        return await client.classify_speakers(chunk, context)

Production Monitoring

from prometheus_client import Counter, Histogram, Gauge
import structlog

class ProductionMonitoring:
    def __init__(self):
        self.documents_processed = Counter('documents_processed_total')
        self.processing_time = Histogram('processing_time_seconds')
        self.active_workers = Gauge('active_workers')
        self.logger = structlog.get_logger()
    
    @self.processing_time.time()
    async def monitored_processing(self, document):
        self.logger.info("Processing started", document_id=document.id)
        try:
            result = await self.process_with_quality_gates(document)
            self.documents_processed.inc()
            return result
        except Exception as e:
            self.logger.error("Processing failed", error=str(e))