Batch Text-to-Speech: How to Generate Thousands of Audio Files at Scale

Batch processing for text-to-speech (TTS) systems can significantly reduce costs by 40-60% when properly architected, offering advantages over real-time streaming when handling high volumes of audio file generation. Key infrastructure decisions, such as leveraging serverless architecture, caching, and volume-tier discounts, play a crucial role in these savings. Batch processing is particularly advantageous for applications like content libraries, podcast generation, and asynchronous workflows, where latency is less critical and large volumes of audio need to be generated efficiently. Effective queue architecture is essential for managing 100,000+ TTS requests, requiring distinct processing queues for different voice types due to rate limits on neural voices. Maintaining voice consistency across large batches involves patterns such as checkpoint reinitialization, voice normalization, and quality monitoring. Additionally, implementing strategies like exponential backoff and recovery procedures can help manage queue backups and prevent cascading failures. The decision between batch and real-time TTS depends on factors like volume, latency tolerance, cost sensitivity, and quality requirements, with batch processing offering predictable infrastructure costs and enhanced compliance capabilities.