ZipL-Dialog: Memory-Efficient Long-Form Spoken Dialog Synthesis via Latent Flow Matching

Jul 16, 2026

ZipL-Dialog introduces a method that shifts conditional flow-matching into a 4x time-compressed latent space, enabling memory-efficient synthesis of long-form spoken dialog. The approach reduces peak GPU memory usage by 11.22x and speeds up inference by 2.23x compared to the baseline, while maintaining perceptual naturalness. This is achieved through a deterministic mel autoencoder and optimized hierarchical downsampling.

Why it matters: This work substantially lowers the memory and computational requirements for generating multi-minute spoken dialog in a single pass, making practical zero-shot dialog TTS more feasible.

Full story at: arXiv Audio and Speech Processing ↗