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

• URL: http://arxiv.org/abs/2509.15969v1
• Date: Fri, 19 Sep 2025 13:26:46 GMT
• Title: VoXtream: Full-Stream Text-to-Speech with Extremely Low Latency
• Authors: Nikita Torgashov, Gustav Eje Henter, Gabriel Skantze,
• Abstract summary: 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.<n>VoXtream directly maps incoming phonemes to audio tokens using a monotonic alignment scheme and a dynamic look-ahead that does not delay onset.<n>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
• Score: 17.067283475630095
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 dynamic 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.

Related papers

• Voxtral Realtime [134.66962524291424]
  Voxtral Realtime is a streaming automatic speech recognition model.<n>It matches offline transcription quality at sub-second latency.<n>We release the model weights under the Apache 2.0 license.
  arXiv  Detail & Related papers  (2026-02-11T19:17:10Z)
• Qwen3-TTS Technical Report [64.94647392030824]
  We present the Qwen3-TTS series, a family of advanced multilingual, controllable, robust, and streaming text-to-speech models.<n>Qwen3-TTS supports state-of-the-art 3-second voice cloning and description-based control.<n>Qwen3-TTS adopts a dual-track LM architecture for real-time synthesis, coupled with two speech tokenizers.
  arXiv  Detail & Related papers  (2026-01-22T03:51:43Z)
• StreamMel: Real-Time Zero-shot Text-to-Speech via Interleaved Continuous Autoregressive Modeling [50.537794606598254]
  StreamMel is a pioneering single-stage streaming TTS framework that models continuous mel-spectrograms.<n>It enables low-latency, autoregressive synthesis while preserving high speaker similarity and naturalness.<n>It even achieves performance comparable to offline systems while supporting efficient real-time generation.
  arXiv  Detail & Related papers  (2025-06-14T16:53:39Z)
• SpeakStream: Streaming Text-to-Speech with Interleaved Data [11.131427505801062]
  We present SpeakStream, a streaming TTS system that generates audio incrementally from streaming text using a decoder-only architecture.<n>During inference, SpeakStream generates speech incrementally while absorbing streaming input text.<n>Our experiments demonstrate that SpeakStream achieves state-of-the-art latency while maintaining the quality of non-streaming TTS systems.
  arXiv  Detail & Related papers  (2025-05-25T16:11:10Z)
• Spark-TTS: An Efficient LLM-Based Text-to-Speech Model with Single-Stream Decoupled Speech Tokens [31.575335190916995]
  We introduce Spark-TTS, a novel system powered by BiCodec, a single-stream speech that decomposes speech into two complementary token types.<n>To facilitate research in controllable TTS, we introduce VoxBox, a meticulously curated 100,000-hour dataset with comprehensive attribute annotations.
  arXiv  Detail & Related papers  (2025-03-03T16:23:10Z)
• SyncSpeech: Low-Latency and Efficient Dual-Stream Text-to-Speech based on Temporal Masked Transformer [68.78023656892319]
  This paper presents a dual-stream text-to-speech (TTS) model, SyncSpeech, capable of receiving streaming text input from upstream models while simultaneously generating streaming speech.<n>SyncSpeech has the following advantages: Low latency, as it begins generating streaming speech upon receiving the second text token; High efficiency, as it decodes all speech tokens corresponding to the each arrived text token in one step.
  arXiv  Detail & Related papers  (2025-02-16T12:14:17Z)
• NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers [90.83782600932567]
  We develop NaturalSpeech 2, a TTS system that leverages a neural audio predictor with residual vectorizers to get the quantized latent vectors. We scale NaturalSpeech 2 to large-scale datasets with 44K hours of speech and singing data and evaluate its voice quality on unseen speakers. NaturalSpeech 2 outperforms previous TTS systems by a large margin in terms of prosody/timbre similarity, synthesis, and voice quality in a zero-shot setting.
  arXiv  Detail & Related papers  (2023-04-18T16:31:59Z)
• MultiSpeech: Multi-Speaker Text to Speech with Transformer [145.56725956639232]
  Transformer-based text to speech (TTS) model (e.g., Transformer TTSciteli 2019neural, FastSpeechciteren 2019fastspeech) has shown the advantages of training and inference efficiency over RNN-based model. We develop a robust and high-quality multi-speaker Transformer TTS system called MultiSpeech, with several specially designed components/techniques to improve text-to-speech alignment.
  arXiv  Detail & Related papers  (2020-06-08T15:05:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.