SyncSpeech: Low-Latency and Efficient Dual-Stream Text-to-Speech based on Temporal Masked Transformer

• URL: http://arxiv.org/abs/2502.11094v1
• Date: Sun, 16 Feb 2025 12:14:17 GMT
• Title: SyncSpeech: Low-Latency and Efficient Dual-Stream Text-to-Speech based on Temporal Masked Transformer
• Authors: Zhengyan Sheng, Zhihao Du, Shiliang Zhang, Zhijie Yan, Yexin Yang, Zhenhua Ling,
• Abstract summary: 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. 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.
• Score: 68.78023656892319
• License:
• Abstract: 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, facilitating seamless interaction with large language models. 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. To achieve this, we propose a temporal masked transformer as the backbone of SyncSpeech, combined with token-level duration prediction to predict speech tokens and the duration for the next step. Additionally, we design a two-stage training strategy to improve training efficiency and the quality of generated speech. We evaluated the SyncSpeech on both English and Mandarin datasets. Compared to the recent dual-stream TTS models, SyncSpeech significantly reduces the first packet delay of speech tokens and accelerates the real-time factor. Moreover, with the same data scale, SyncSpeech achieves performance comparable to that of traditional autoregressive-based TTS models in terms of both speech quality and robustness. Speech samples are available at https://SyncSpeech.github.io/}{https://SyncSpeech.github.io/.

Related papers

• A Non-autoregressive Generation Framework for End-to-End Simultaneous Speech-to-Speech Translation [48.84039953531355]
  We propose a novel non-autoregressive generation framework for simultaneous speech translation (NAST-S2X) NAST-S2X integrates speech-to-text and speech-to-speech tasks into a unified end-to-end framework. It achieves high-quality simultaneous interpretation within a delay of less than 3 seconds and provides a 28 times decoding speedup in offline generation.
  arXiv  Detail & Related papers  (2024-06-11T04:25:48Z)
• StreamSpeech: Simultaneous Speech-to-Speech Translation with Multi-task Learning [48.84039953531356]
  StreamSpeech is a direct Simul-S2ST model that jointly learns translation and simultaneous policy. Experiments on CVSS benchmark demonstrate that StreamSpeech achieves state-of-the-art performance in both offline S2ST and Simul-S2ST tasks.
  arXiv  Detail & Related papers  (2024-06-05T08:24:22Z)
• DASpeech: Directed Acyclic Transformer for Fast and High-quality Speech-to-Speech Translation [36.126810842258706]
  Direct speech-to-speech translation (S2ST) translates speech from one language into another using a single model. Due to the presence of linguistic and acoustic diversity, the target speech follows a complex multimodal distribution. We propose DASpeech, a non-autoregressive direct S2ST model which realizes both fast and high-quality S2ST.
  arXiv  Detail & Related papers  (2023-10-11T11:39:36Z)
• 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)
• AdaSpeech 2: Adaptive Text to Speech with Untranscribed Data [115.38309338462588]
  We develop AdaSpeech 2, an adaptive TTS system that only leverages untranscribed speech data for adaptation. Specifically, we introduce a mel-spectrogram encoder to a well-trained TTS model to conduct speech reconstruction. In adaptation, we use untranscribed speech data for speech reconstruction and only fine-tune the TTS decoder.
  arXiv  Detail & Related papers  (2021-04-20T01:53:30Z)
• 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.