# VITS2: Improving Quality and Efficiency of Single-Stage Text-to-Speech with Adversarial Learning and Architecture Design

arXiv: https://arxiv.org/abs/2307.16430 · HTML: https://mlsanity.com/paper/2307.16430/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 24 references that are in the map.
- First posted on arXiv 31 Jul 2023.

## Builds on (most cited first, 19 of 19)

- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://mlsanity.com/paper/1712.05884/) (2017)
- [FastSpeech 2: Fast and High-Quality End-to-End Text to Speech](https://mlsanity.com/paper/2006.04558/) (2020)
- [WaveGrad: Estimating Gradients for Waveform Generation](https://mlsanity.com/paper/2009.00713/) (2020)
- [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://mlsanity.com/paper/2106.06103/) (2021)
- [Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech](https://mlsanity.com/paper/2105.06337/) (2021)
- [MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis](https://mlsanity.com/paper/1910.06711/) (2019)
- [Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram](https://mlsanity.com/paper/1910.11480/) (2019)
- [WaveGlow: A Flow-based Generative Network for Speech Synthesis](https://mlsanity.com/paper/1811.00002/) (2018)
- [Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis](https://mlsanity.com/paper/1806.04558/) (2018)
- [Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search](https://mlsanity.com/paper/2005.11129/) (2020)
- [Efficient Neural Audio Synthesis](https://mlsanity.com/paper/1802.08435/) (2018)
- [NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Quality](https://mlsanity.com/paper/2205.04421/) (2022)
- [High Fidelity Speech Synthesis with Adversarial Networks](https://mlsanity.com/paper/1909.11646/) (2019)
- [End-to-End Adversarial Text-to-Speech](https://mlsanity.com/paper/2006.03575/) (2020)
- [Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis](https://mlsanity.com/paper/2005.05957/) (2020)
- [JETS: Jointly Training FastSpeech2 and HiFi-GAN for End to End Text to Speech](https://mlsanity.com/paper/2203.16852/) (2022)

## Built on by (most cited first, 25 of 25)

- [WavChat: A Survey of Spoken Dialogue Models](https://mlsanity.com/paper/2411.13577/) (2024)
- [SpeechCraft: A Fine-grained Expressive Speech Dataset with Natural Language Description](https://mlsanity.com/paper/2408.13608/) (2024)
- [WavReward: Spoken Dialogue Models With Generalist Reward Evaluators](https://mlsanity.com/paper/2505.09558/) (2025)
- [TaoAvatar: Real-Time Lifelike Full-Body Talking Avatars for Augmented Reality via 3D Gaussian Splatting](https://mlsanity.com/paper/2503.17032/) (2025)
- [Accelerating Autoregressive Speech Synthesis Inference With Speech Speculative Decoding](https://mlsanity.com/paper/2505.15380/) (2025)
- [M3-TTS: Multi-modal DiT Alignment & Mel-latent for Zero-shot High-fidelity Speech Synthesis](https://mlsanity.com/paper/2512.04720/) (2025)
- [Fake it to make it: Using synthetic data to remedy the data shortage in joint multimodal speech-and-gesture synthesis](https://mlsanity.com/paper/2404.19622/) (2024)
- [DiEmo-TTS: Disentangled Emotion Representations via Self-Supervised Distillation for Cross-Speaker Emotion Transfer in Text-to-Speech](https://mlsanity.com/paper/2505.19687/) (2025)
- [FaceSpeak: Expressive and High-Quality Speech Synthesis from Human Portraits of Different Styles](https://mlsanity.com/paper/2501.03181/) (2025)
- [MathReader : Text-to-Speech for Mathematical Documents](https://mlsanity.com/paper/2501.07088/) (2025)
- [FMSD-TTS: Few-shot Multi-Speaker Multi-Dialect Text-to-Speech Synthesis for "U-Tsang, Amdo and Kham Speech Dataset Generation](https://mlsanity.com/paper/2505.14351/) (2025)
- [Multi-modal Adversarial Training for Zero-Shot Voice Cloning](https://mlsanity.com/paper/2408.15916/) (2024)
- [Towards Better Disentanglement in Non-Autoregressive Zero-Shot Expressive Voice Conversion](https://mlsanity.com/paper/2506.04013/) (2025)
- [VCounselor: A Psychological Intervention Chat Agent Based on a Knowledge-Enhanced Large Language Model](https://mlsanity.com/paper/2403.13553/) (2024)
- [Comparative Evaluation of Expressive Japanese Character Text-to-Speech with VITS and Style-BERT-VITS2](https://mlsanity.com/paper/2505.17320/) (2025)
- [Rethinking Internet Communication Through LLMs: How Close Are We?](https://mlsanity.com/paper/2309.14247/) (2023)
- [A multi-speaker multi-lingual voice cloning system based on vits2 for limmits 2024 challenge](https://mlsanity.com/paper/2406.17801/) (2024)
- [Clip-TTS: Contrastive Text-content and Mel-spectrogram, A High-Quality Text-to-Speech Method based on Contextual Semantic Understanding](https://mlsanity.com/paper/2502.18889/) (2025)
- [Balalaika: Data-Centric, Prosody-Aware Annotation Pipeline for Russian Speech](https://mlsanity.com/paper/2507.13563/) (2025)
- [FNH-TTS: Mixture-of-Experts Duration Modeling for Robust Neural Speech Synthesis](https://mlsanity.com/paper/2508.12001/) (2025)
- [TMD-TTS: A Unified Tibetan Multi-Dialect Text-to-Speech Framework for "U-Tsang, Amdo and Kham Speech Dataset Generation](https://mlsanity.com/paper/2509.18060/) (2025)
- [Flamed-TTS: Flow Matching Attention-Free Models for Efficient Generating and Dynamic Pacing Zero-shot Text-to-Speech](https://mlsanity.com/paper/2510.02848/) (2025)
- [Vagdhenu: A Vrutta (Meter) Aware Shloka-to-Chant (TTS) System for Sanskrit](https://mlsanity.com/paper/2608.26146/) (2026)
- [Morpho-VITS: Variational Inference with Morphological Modeling for End-to-End Speech Synthesis of a Tonal Bantu Language](https://mlsanity.com/paper/2609.24310/) (2026)
- [BanglaKontho: Closing the Long-Form Gap in Bangla Text-to-Speech](https://mlsanity.com/paper/2609.29146/) (2026)

## Abstract (arXiv)

Single-stage text-to-speech models have been actively studied recently, and their results have outperformed two-stage pipeline systems. Although the previous single-stage model has made great progress, there is room for improvement in terms of its intermittent unnaturalness, computational efficiency, and strong dependence on phoneme conversion. In this work, we introduce VITS2, a single-stage text-to-speech model that efficiently synthesizes a more natural speech by improving several aspects of the previous work. We propose improved structures and training mechanisms and present that the proposed methods are effective in improving naturalness, similarity of speech characteristics in a multi-speaker model, and efficiency of training and inference. Furthermore, we demonstrate that the strong dependence on phoneme conversion in previous works can be significantly reduced with our method, which allows a fully end-to-end single-stage approach.