StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models
On the map: Diffusion models → Audio, speech and music
Abstract (arXiv)
In this paper, we present StyleTTS 2, a text-to-speech (TTS) model that leverages style diffusion and adversarial training with large speech language models (SLMs) to achieve human-level TTS synthesis. StyleTTS 2 differs from its predecessor by modeling styles as a latent random variable through diffusion models to generate the most suitable style for the text without requiring reference speech, achieving efficient latent diffusion while benefiting from the diverse speech synthesis offered by diffusion models. Furthermore, we employ large pre-trained SLMs, such as WavLM, as discriminators with our novel differentiable duration modeling for end-to-end training, resulting in improved speech naturalness. StyleTTS 2 surpasses human recordings on the single-speaker LJSpeech dataset and matches it on the multispeaker VCTK dataset as judged by native English speakers. Moreover, when trained on the LibriTTS dataset, our model outperforms previous publicly available models for zero-shot speaker adaptation. This work achieves the first human-level TTS on both single and multispeaker datasets, showcasing the potential of style diffusion and adversarial training with large SLMs. The audio demos and source code are available at https://styletts2.github.io/.
Key facts. Cited by 152 papers in the map (data through 14 Oct 2026). Placed in Audio, speech and music, one of 14 directions in Diffusion models. Builds on 57 of its 65 references that are in the map. First posted on arXiv 13 Jun 2023.
Builds on
57 of 65 references.
Closest ancestors · 14
Same direction, newest first
- 2023NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers18 Apr
- 2023A Survey on Audio Diffusion Models: Text To Speech Synthesis and Enhancement in Generative AI23 Mar
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- 2022WaveFit: An Iterative and Non-autoregressive Neural Vocoder based on Fixed-Point Iteration3 Oct
- 2022Diffsound: Discrete Diffusion Model for Text-to-sound Generation20 Jul
- 2022ProDiff: Progressive Fast Diffusion Model For High-Quality Text-to-Speech13 Jul
- 2022FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis21 Apr
- 2022SpecGrad: Diffusion Probabilistic Model based Neural Vocoder with Adaptive Noise Spectral Shaping31 Mar
- 2022InferGrad: Improving Diffusion Models for Vocoder by Considering Inference in Training8 Feb
- 2022DiffGAN-TTS: High-Fidelity and Efficient Text-to-Speech with Denoising Diffusion GANs28 Jan
- 2021PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior11 Jun
- 2021Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech13 May
2 more
Other references · 43
- 2017Attention Is All You Needcited by 60,361
- 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
- 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
- 2022Elucidating the Design Space of Diffusion-Based Generative Modelscited by 3,045
- 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,342
- 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 2,010
- 2022DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Stepscited by 1,979
- 2017Decoupled Weight Decay Regularizationcited by 1,831
- 2021WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processingcited by 1,657
- 2020HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesiscited by 1,154
- 2016Least Squares Generative Adversarial Networkscited by 1,001
- 2023Neural Codec Language Models are Zero-Shot Text to Speech Synthesizerscited by 752
31 more
- 2019LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speechcited by 649
- 2020WaveGrad: Estimating Gradients for Waveform Generationcited by 622
- 2021Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speechcited by 561
- 2018ESPnet: End-to-End Speech Processing Toolkitcited by 558
- 2021Layer-wise Analysis of a Self-supervised Speech Representation Modelcited by 298
- 2021YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyonecited by 278
- 2022BigVGAN: A Universal Neural Vocoder with Large-Scale Trainingcited by 251
- 2021Projected GANs Converge Fastercited by 208
- 2021A Survey on Neural Speech Synthesiscited by 184
- 2021A Variational Perspective on Diffusion-Based Generative Models and Score Matchingcited by 183
- 2022NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Qualitycited by 152
- 2020Neural Networks Fail to Learn Periodic Functions and How to Fix Itcited by 100
- 2020End-to-End Adversarial Text-to-Speechcited by 77
- 2021UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generationcited by 74
- 2020Non-Attentive Tacotron: Robust and Controllable Neural TTS Synthesis Including Unsupervised Duration Modelingcited by 65
- 2021WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesiscited by 65
- 2021StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversioncited by 58
- 2021PnG BERT: Augmented BERT on Phonemes and Graphemes for Neural TTScited by 52
- 2022StyleTTS: A Style-Based Generative Model for Natural and Diverse Text-to-Speech Synthesiscited by 33
- 2022iSTFTNet: Fast and Lightweight Mel-Spectrogram Vocoder Incorporating Inverse Short-Time Fourier Transformcited by 32
- 2022VQTTS: High-Fidelity Text-to-Speech Synthesis with Self-Supervised VQ Acoustic Featurecited by 32
- 2021Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modelingcited by 31
- 2022JETS: Jointly Training FastSpeech2 and HiFi-GAN for End to End Text to Speechcited by 26
- 2023Phoneme-Level BERT for Enhanced Prosody of Text-to-Speech with Grapheme Predictionscited by 23
- 2022WavThruVec: Latent speech representation as intermediate features for neural speech synthesiscited by 17
- 2022Mixed-Phoneme BERT: Improving BERT with Mixed Phoneme and Sup-Phoneme Representations for Text to Speechcited by 13
- 2023Zero-shot text-to-speech synthesis conditioned using self-supervised speech representation modelcited by 10
- 2023Why We Should Report the Details in Subjective Evaluation of TTS More Rigorouslycited by 10
- 2022StyleTTS-VC: One-Shot Voice Conversion by Knowledge Transfer from Style-Based TTS Modelscited by 9
- 2023FoundationTTS: Text-to-Speech for ASR Customization with Generative Language Modelcited by 7
- 2023M2-CTTS: End-to-End Multi-scale Multi-modal Conversational Text-to-Speech Synthesiscited by 6
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Citing papers per year: 2023: 4; 2024: 45; 2025: 53; 2026: 50.
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