Distill Locally, Schedule Globally: Flow Maps for Few-Step Text-to-Speech
Flow-matching text-to-speech (TTS) models achieve high synthesis quality but require many neural function evaluations (NFEs) to integrate their generative trajectories. Recent few-step flow-map distillation approaches for TTS construct targets from numerically integrated teacher trajectories, creating a trade-off between target accuracy and training cost. We propose Local Flow-Map Distillation (LFMD), which adapts Eulerian Map Distillation to conditional TTS and avoids teacher trajectory integration during target construction. For inference, we derive a sampling schedule (TD-DP) from teacher dynamics and consistency of the learned maps, with a single cost graph supporting multiple NFE bud...
| Authors | Yentl Collin, Evan Dufraisse, Amr Mohamed et al. |
|---|---|
| Primary category | cs.SD |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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