Small-E: Small Language Model with Linear Attention for Efficient Speech Synthesis

• URL: http://arxiv.org/abs/2406.04467v2
• Date: Tue, 11 Jun 2024 12:36:36 GMT
• Title: Small-E: Small Language Model with Linear Attention for Efficient Speech Synthesis
• Authors: Théodor Lemerle, Nicolas Obin, Axel Roebel,
• Abstract summary: Recent advancements in text-to-speech (TTS) powered by language models have showcased remarkable capabilities in achieving naturalness and zero-shot voice cloning. We propose to replace transformers with emerging recurrent architectures and introduce specialized cross-attention mechanisms for reducing repeating and skipping issues. Our architecture can be efficiently trained on long samples and achieve state-of-the-art zero-shot voice cloning against baselines of comparable size.
• Score: 7.865191493201841
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in text-to-speech (TTS) powered by language models have showcased remarkable capabilities in achieving naturalness and zero-shot voice cloning. Notably, the decoder-only transformer is the prominent architecture in this domain. However, transformers face challenges stemming from their quadratic complexity in sequence length, impeding training on lengthy sequences and resource-constrained hardware. Moreover they lack specific inductive bias with regards to the monotonic nature of TTS alignments. In response, we propose to replace transformers with emerging recurrent architectures and introduce specialized cross-attention mechanisms for reducing repeating and skipping issues. Consequently our architecture can be efficiently trained on long samples and achieve state-of-the-art zero-shot voice cloning against baselines of comparable size. Our implementation and demos are available at https://github.com/theodorblackbird/lina-speech.

Related papers

• Very Attentive Tacotron: Robust and Unbounded Length Generalization in Autoregressive Transformer-Based Text-to-Speech [9.982121768809854]
  We introduce enhancements aimed at AR Transformer-based encoder-decoder text-to-speech systems. Our approach uses an alignment mechanism to provide cross-attention operations with relative location information. A system incorporating these improvements, which we call Very Attentive Tacotron, matches the naturalness and expressiveness of a baseline T5-based TTS system.
  arXiv  Detail & Related papers  (2024-10-29T16:17:01Z)
• Multi-modal Adversarial Training for Zero-Shot Voice Cloning [9.823246184635103]
  We propose a Transformer encoder-decoder architecture to conditionally discriminate between real and generated speech features. We introduce our novel adversarial training technique by applying it to a FastSpeech2 acoustic model and training on Libriheavy, a large multi-speaker dataset. Our model achieves improvements over the baseline in terms of speech quality and speaker similarity.
  arXiv  Detail & Related papers  (2024-08-28T16:30:41Z)
• DiTTo-TTS: Efficient and Scalable Zero-Shot Text-to-Speech with Diffusion Transformer [9.032701216955497]
  We present an efficient and scalable Diffusion Transformer (DiT) that utilizes off-the-shelf pre-trained text and speech encoders. Our approach addresses the challenge of text-speech alignment via cross-attention mechanisms with the prediction of the total length of speech representations. We scale the training dataset and the model size to 82K hours and 790M parameters, respectively.
  arXiv  Detail & Related papers  (2024-06-17T11:25:57Z)
• 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)
• Generative Pre-trained Speech Language Model with Efficient Hierarchical Transformer [39.31849739010572]
  We introduce textbfGenerative textbfPre-trained textbfSpeech textbfTransformer (GPST) GPST is a hierarchical transformer designed for efficient speech language modeling.
  arXiv  Detail & Related papers  (2024-06-03T04:16:30Z)
• Pheme: Efficient and Conversational Speech Generation [52.34331755341856]
  We introduce the Pheme model series that offers compact yet high-performing conversational TTS models. It can be trained efficiently on smaller-scale conversational data, cutting data demands by more than 10x but still matching the quality of the autoregressive TTS models.
  arXiv  Detail & Related papers  (2024-01-05T14:47:20Z)
• Mega-TTS: Zero-Shot Text-to-Speech at Scale with Intrinsic Inductive Bias [71.94109664001952]
  Mega-TTS is a novel zero-shot TTS system that is trained with large-scale wild data. We show that Mega-TTS surpasses state-of-the-art TTS systems on zero-shot TTS speech editing, and cross-lingual TTS tasks.
  arXiv  Detail & Related papers  (2023-06-06T08:54:49Z)
• 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)
• Pretraining Techniques for Sequence-to-Sequence Voice Conversion [57.65753150356411]
  Sequence-to-sequence (seq2seq) voice conversion (VC) models are attractive owing to their ability to convert prosody. We propose to transfer knowledge from other speech processing tasks where large-scale corpora are easily available, typically text-to-speech (TTS) and automatic speech recognition (ASR) We argue that VC models with such pretrained ASR or TTS model parameters can generate effective hidden representations for high-fidelity, highly intelligible converted speech.
  arXiv  Detail & Related papers  (2020-08-07T11:02:07Z)
• 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.