Lite-TTS

Lite-TTS is a high-performance neural text-to-speech framework engineered for the 2026 landscape of decentralized and edge-based AI. Built on a streamlined transformer architecture, Lite-TTS focuses on minimizing the computational footprint while maintaining a high Mean Opinion Score (MOS). Unlike heavy-duty cloud-based models that suffer from network jitter, Lite-TTS is optimized for local inference using ONNX and TensorRT runtimes. Its core architecture utilizes a decoupled approach, separating the linguistic front-end from a high-speed vocoder, typically utilizing BigVGAN or HiFi-GAN derivatives. This allows for sub-150ms 'Time to First Audio' (TTFA), making it ideal for interactive NPCs in gaming, responsive IoT interfaces, and localized accessibility services. By 2026, Lite-TTS has positioned itself as the industry standard for privacy-conscious developers who require high-quality vocal output without the data egress costs or latency overhead of traditional SaaS providers. It supports complex SSML 1.1 tags, enabling granular control over pitch, prosody, and emotional inflection across more than 40 languages.