English

VoXtream: Full-Stream Text-to-Speech with Extremely Low Latency

Audio and Speech Processing 2026-01-27 v2 Computation and Language Human-Computer Interaction Machine Learning Sound

Abstract

We present VoXtream, a fully autoregressive, zero-shot streaming text-to-speech (TTS) system for real-time use that begins speaking from the first word. VoXtream directly maps incoming phonemes to audio tokens using a monotonic alignment scheme and a limited look-ahead that does not delay onset. Built around an incremental phoneme transformer, a temporal transformer predicting semantic and duration tokens, and a depth transformer producing acoustic tokens, VoXtream achieves, to our knowledge, the lowest initial delay among publicly available streaming TTS: 102 ms on GPU. Despite being trained on a mid-scale 9k-hour corpus, it matches or surpasses larger baselines on several metrics, while delivering competitive quality in both output- and full-streaming settings. Demo and code are available at https://herimor.github.io/voxtream.

Keywords

Cite

@article{arxiv.2509.15969,
  title  = {VoXtream: Full-Stream Text-to-Speech with Extremely Low Latency},
  author = {Nikita Torgashov and Gustav Eje Henter and Gabriel Skantze},
  journal= {arXiv preprint arXiv:2509.15969},
  year   = {2026}
}

Comments

5 pages, 1 figure, accepted to IEEE ICASSP 2026

R2 v1 2026-07-01T05:45:49.490Z