VoXtream: Full-Stream Text-to-Speech with Extremely Low Latency
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arXiv
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| Format: | Preprint |
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2025
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| author | Torgashov, Nikita Henter, Gustav Eje Skantze, Gabriel |
| author_facet | Torgashov, Nikita Henter, Gustav Eje Skantze, Gabriel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_15969 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VoXtream: Full-Stream Text-to-Speech with Extremely Low Latency Torgashov, Nikita Henter, Gustav Eje Skantze, Gabriel Audio and Speech Processing Computation and Language Human-Computer Interaction Machine Learning Sound 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. |
| title | VoXtream: Full-Stream Text-to-Speech with Extremely Low Latency |
| topic | Audio and Speech Processing Computation and Language Human-Computer Interaction Machine Learning Sound |
| url | https://arxiv.org/abs/2509.15969 |