Text-to-Speech Architecture: Production Tradeoffs for Voice AI

Text-to-speech (TTS) architecture plays a crucial role in determining the success of voice applications in production environments by impacting latency, concurrency, and cost. The article explores how modern TTS systems, including autoregressive and non-autoregressive architectures, perform under these constraints and emphasizes the importance of selecting architectures based on operational requirements rather than solely on voice quality. Non-autoregressive systems like FastSpeech 2 excel in environments requiring sub-100ms latency for real-time interactions, while autoregressive models such as Tacotron 2 are more suited for applications like audiobook production where latency tolerance is higher. Efficient vocoders like HiFi-GAN enhance performance by reducing waveform synthesis overhead, enabling systems to achieve high mean opinion scores (MOS) with minimal latency. The article advises prioritizing infrastructure optimization and transparent cost structures when assessing TTS solutions, highlighting the need for a constraint-first approach in architecture selection to ensure scalability and economic viability as user demands grow.