Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis

• URL: http://arxiv.org/abs/2005.05957v3
• Date: Thu, 16 Jul 2020 15:10:18 GMT
• Title: Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis
• Authors: Rafael Valle, Kevin Shih, Ryan Prenger, Bryan Catanzaro
• Abstract summary: Flowtron is an autoregressive flow-based generative network for text-to-speech synthesis. We provide results on control of speech variation, between samples and style transfer between speakers seen and unseen during training.
• Score: 23.115879727598262
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper we propose Flowtron: an autoregressive flow-based generative network for text-to-speech synthesis with control over speech variation and style transfer. Flowtron borrows insights from IAF and revamps Tacotron in order to provide high-quality and expressive mel-spectrogram synthesis. Flowtron is optimized by maximizing the likelihood of the training data, which makes training simple and stable. Flowtron learns an invertible mapping of data to a latent space that can be manipulated to control many aspects of speech synthesis (pitch, tone, speech rate, cadence, accent). Our mean opinion scores (MOS) show that Flowtron matches state-of-the-art TTS models in terms of speech quality. In addition, we provide results on control of speech variation, interpolation between samples and style transfer between speakers seen and unseen during training. Code and pre-trained models will be made publicly available at https://github.com/NVIDIA/flowtron

Related papers

• Pseudo-Autoregressive Neural Codec Language Models for Efficient Zero-Shot Text-to-Speech Synthesis [64.12708207721276]
  We introduce a novel pseudo-autoregressive (PAR) language modeling approach that unifies AR and NAR modeling. Building on PAR, we propose PALLE, a two-stage TTS system that leverages PAR for initial generation followed by NAR refinement. Experiments demonstrate that PALLE, trained on LibriTTS, outperforms state-of-the-art systems trained on large-scale data.
  arXiv  Detail & Related papers  (2025-04-14T16:03:21Z)
• V2SFlow: Video-to-Speech Generation with Speech Decomposition and Rectified Flow [57.51550409392103]
  We introduce V2SFlow, a novel Video-to-Speech (V2S) framework designed to generate natural and intelligible speech directly from silent talking face videos. To address these challenges, we decompose the speech signal into manageable subspaces, each representing distinct speech attributes, and predict them directly from the visual input. To generate coherent and realistic speech from these predicted attributes, we employ a rectified flow matching decoder built on a Transformer architecture.
  arXiv  Detail & Related papers  (2024-11-29T05:55:20Z)
• 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)
• VQ-CTAP: Cross-Modal Fine-Grained Sequence Representation Learning for Speech Processing [81.32613443072441]
  For tasks such as text-to-speech (TTS), voice conversion (VC), and automatic speech recognition (ASR), a cross-modal fine-grained (frame-level) sequence representation is desired. We propose a method called Quantized Contrastive Token-Acoustic Pre-training (VQ-CTAP), which uses the cross-modal sequence transcoder to bring text and speech into a joint space.
  arXiv  Detail & Related papers  (2024-08-11T12:24:23Z)
• Unsupervised Pre-Training For Data-Efficient Text-to-Speech On Low Resource Languages [15.32264927462068]
  We propose an unsupervised pre-training method for a sequence-to-sequence TTS model by leveraging large untranscribed speech data. The main idea is to pre-train the model to reconstruct de-warped mel-spectrograms from warped ones. We empirically demonstrate the effectiveness of our proposed method in low-resource language scenarios.
  arXiv  Detail & Related papers  (2023-03-28T01:26:00Z)
• A Vector Quantized Approach for Text to Speech Synthesis on Real-World Spontaneous Speech [94.64927912924087]
  We train TTS systems using real-world speech from YouTube and podcasts. Recent Text-to-Speech architecture is designed for multiple code generation and monotonic alignment. We show thatRecent Text-to-Speech architecture outperforms existing TTS systems in several objective and subjective measures.
  arXiv  Detail & Related papers  (2023-02-08T17:34:32Z)
• Predicting phoneme-level prosody latents using AR and flow-based Prior Networks for expressive speech synthesis [3.6159128762538018]
  We show that normalizing flow based prior networks can result in more expressive speech at the cost of a slight drop in quality. We also propose a Dynamical VAE model, that can generate higher quality speech although with decreased expressiveness and variability compared to the flow based models.
  arXiv  Detail & Related papers  (2022-11-02T17:45:01Z)
• Enhanced Direct Speech-to-Speech Translation Using Self-supervised Pre-training and Data Augmentation [76.13334392868208]
  Direct speech-to-speech translation (S2ST) models suffer from data scarcity issues. In this work, we explore self-supervised pre-training with unlabeled speech data and data augmentation to tackle this issue.
  arXiv  Detail & Related papers  (2022-04-06T17:59:22Z)
• JETS: Jointly Training FastSpeech2 and HiFi-GAN for End to End Text to Speech [7.476901945542385]
  We present end-to-end text-to-speech (E2E-TTS) model which has a simplified training pipeline and outperforms a cascade of separately learned models. Our proposed model is jointly trained FastSpeech2 and HiFi-GAN with an alignment module. Experiments on LJSpeech corpus shows that the proposed model outperforms publicly available, state-of-the-art implementations of ESPNet2-TTS.
  arXiv  Detail & Related papers  (2022-03-31T07:25:11Z)
• GANtron: Emotional Speech Synthesis with Generative Adversarial Networks [0.0]
  We propose a text-to-speech model where the inferred speech can be tuned with the desired emotions. We use Generative Adversarial Networks (GANs) together with a sequence-to-sequence model using an attention mechanism.
  arXiv  Detail & Related papers  (2021-10-06T10:44:30Z)
• Wave-Tacotron: Spectrogram-free end-to-end text-to-speech synthesis [25.234945748885348]
  We describe a sequence-to-sequence neural network which directly generates speech waveforms from text inputs. The architecture extends the Tacotron model by incorporating a normalizing flow into the autoregressive decoder loop. Experiments show that the proposed model generates speech with quality approaching a state-of-the-art neural TTS system.
  arXiv  Detail & Related papers  (2020-11-06T19:30:07Z)
• 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)
• Relative Positional Encoding for Speech Recognition and Direct Translation [72.64499573561922]
  We adapt the relative position encoding scheme to the Speech Transformer. As a result, the network can better adapt to the variable distributions present in speech data.
  arXiv  Detail & Related papers  (2020-05-20T09:53:06Z)

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.