Project Title

vits — End-to-End Text-to-Speech Synthesis with Adversarial Learning and Variational Autoencoder

Overview

VITS is an open-source Python project that implements a Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech synthesis. It aims to generate more natural sounding audio compared to current two-stage TTS systems by adopting variational inference, normalizing flows, and adversarial training. VITS stands out for its ability to synthesize speech with diverse rhythms from input text, leveraging uncertainty modeling over latent variables and a stochastic duration predictor.

Key Features

Parallel end-to-end TTS method for natural audio generation
Variational inference with normalizing flows and adversarial training
Stochastic duration predictor for diverse rhythm synthesis
Subjective human evaluation showing superior performance over publicly available TTS systems

Use Cases

Researchers and developers in the field of speech synthesis can use VITS to create more natural and diverse audio outputs from text inputs.
Companies developing voice assistants or other voice-based applications can leverage VITS to enhance the quality and expressiveness of synthesized speech.
Educational institutions can utilize VITS for teaching and research purposes in speech technology and machine learning.

Advantages

Achieves a mean opinion score (MOS) comparable to ground truth, outperforming other publicly available TTS systems.
Offers a more natural one-to-many relationship in text-to-speech conversion, allowing for multiple ways to speak the same text with different pitches and rhythms.
Provides pretrained models and an interactive demo for quick experimentation and understanding.

Limitations / Considerations

The project requires a significant amount of computational resources and expertise in machine learning and speech synthesis to effectively train and utilize the models.
The quality of synthesized speech may be dependent on the quality and size of the training datasets.
As with any machine learning model, there may be biases present in the training data that could affect the output.

Tacotron 2: A popular open-source text-to-speech synthesis model known for its high-quality audio output, but it requires a two-stage process.
WaveNet: A deep generative model for raw audio waveforms, which can be used for TTS but is computationally expensive and requires large datasets.
FastSpeech: A text-to-speech model that claims to generate speech faster than real-time, but may not match the naturalness of VITS.

Basic Information

GitHub: https://github.com/jaywalnut310/vits
Stars: 7,703
License: Unknown
Last Commit: 2025-10-09

📊 Project Information

Project Name: vits
GitHub URL: https://github.com/jaywalnut310/vits
Programming Language: Python
⭐ Stars: 7,703
🍴 Forks: 1,377
📅 Created: 2021-05-26
🔄 Last Updated: 2025-10-09

🏷️ Project Topics

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🎮 Online Demos

demo

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vits

Project Description