Fast and High-Quality Auto-Regressive Speech Synthesis via Speculative Decoding

• URL: http://arxiv.org/abs/2410.21951v1
• Date: Tue, 29 Oct 2024 11:12:01 GMT
• Title: Fast and High-Quality Auto-Regressive Speech Synthesis via Speculative Decoding
• Authors: Bohan Li, Hankun Wang, Situo Zhang, Yiwei Guo, Kai Yu,
• Abstract summary: We introduce VADUSA, one of the first approaches to accelerate auto-regressive TTS through speculative decoding. Our results show that VADUSA not only significantly improves inference speed but also enhances performance by incorporating draft heads to predict future speech content auto-regressively.
• Score: 11.128340782271305
• License:
• Abstract: The auto-regressive architecture, like GPTs, is widely used in modern Text-to-Speech (TTS) systems. However, it incurs substantial inference time, particularly due to the challenges in the next-token prediction posed by lengthy sequences of speech tokens. In this work, we introduce VADUSA, one of the first approaches to accelerate auto-regressive TTS through speculative decoding. Our results show that VADUSA not only significantly improves inference speed but also enhances performance by incorporating draft heads to predict future speech content auto-regressively. Furthermore, the inclusion of a tolerance mechanism during sampling accelerates inference without compromising quality. Our approach demonstrates strong generalization across large datasets and various types of speech tokens.

Related papers

• SimpleSpeech 2: Towards Simple and Efficient Text-to-Speech with Flow-based Scalar Latent Transformer Diffusion Models [64.40250409933752]
  We build upon our previous publication by implementing a simple and efficient non-autoregressive (NAR) TTS framework, termed SimpleSpeech 2. SimpleSpeech 2 effectively combines the strengths of both autoregressive (AR) and non-autoregressive (NAR) methods. We show a significant improvement in generation performance and generation speed compared to our previous work and other state-of-the-art (SOTA) large-scale TTS models.
  arXiv  Detail & Related papers  (2024-08-25T17:07:39Z)
• VALL-E R: Robust and Efficient Zero-Shot Text-to-Speech Synthesis via Monotonic Alignment [101.2489492032816]
  VALL-E R is a robust and efficient zero-shot Text-to-Speech system. This research has the potential to be applied to meaningful projects, including the creation of speech for those affected by aphasia.
  arXiv  Detail & Related papers  (2024-06-12T04:09:44Z)
• Incremental FastPitch: Chunk-based High Quality Text to Speech [0.7366405857677227]
  We propose Incremental FastPitch, a novel FastPitch variant capable of incrementally producing high-quality Mel chunks. Experimental results show that our proposal can produce speech quality comparable to the parallel FastPitch.
  arXiv  Detail & Related papers  (2024-01-03T14:17:35Z)
• SpeechUT: Bridging Speech and Text with Hidden-Unit for Encoder-Decoder Based Speech-Text Pre-training [106.34112664893622]
  We propose a unified-modal speech-unit-text pre-training model, SpeechUT, to connect the representations of a speech encoder and a text decoder with a shared unit encoder. Our proposed SpeechUT is fine-tuned and evaluated on automatic speech recognition (ASR) and speech translation (ST) tasks.
  arXiv  Detail & Related papers  (2022-10-07T17:57:45Z)
• TranSpeech: Speech-to-Speech Translation With Bilateral Perturbation [61.564874831498145]
  TranSpeech is a speech-to-speech translation model with bilateral perturbation. We establish a non-autoregressive S2ST technique, which repeatedly masks and predicts unit choices. TranSpeech shows a significant improvement in inference latency, enabling speedup up to 21.4x than autoregressive technique.
  arXiv  Detail & Related papers  (2022-05-25T06:34:14Z)
• Tokenwise Contrastive Pretraining for Finer Speech-to-BERT Alignment in End-to-End Speech-to-Intent Systems [31.18865184576272]
  This work is a step towards doing the same in a much more efficient and fine-grained manner where we align speech embeddings and BERT embeddings on a token-by-token basis. We introduce a simple yet novel technique that uses a cross-modal attention mechanism to extract token-level contextual embeddings from a speech encoder. Fine-tuning such a pretrained model to perform intent recognition using speech directly yields state-of-the-art performance on two widely used SLU datasets.
  arXiv  Detail & Related papers  (2022-04-11T15:24:25Z)
• Semi-Autoregressive Image Captioning [153.9658053662605]
  Current state-of-the-art approaches for image captioning typically adopt an autoregressive manner. Non-autoregressive image captioning with continuous iterative refinement can achieve comparable performance to the autoregressive counterparts with a considerable acceleration. We propose a novel two-stage framework, referred to as Semi-Autoregressive Image Captioning (SAIC) to make a better trade-off between performance and speed.
  arXiv  Detail & Related papers  (2021-10-11T15:11:54Z)
• STYLER: Style Modeling with Rapidity and Robustness via SpeechDecomposition for Expressive and Controllable Neural Text to Speech [2.622482339911829]
  STYLER is a novel expressive text-to-speech model with parallelized architecture. Our novel noise modeling approach from audio using domain adversarial training and Residual Decoding enabled style transfer without transferring noise.
  arXiv  Detail & Related papers  (2021-03-17T07:11:09Z)
• Fast End-to-End Speech Recognition via a Non-Autoregressive Model and Cross-Modal Knowledge Transferring from BERT [72.93855288283059]
  We propose a non-autoregressive speech recognition model called LASO (Listen Attentively, and Spell Once) The model consists of an encoder, a decoder, and a position dependent summarizer (PDS)
  arXiv  Detail & Related papers  (2021-02-15T15:18:59Z)

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.