--- title: "vits" type: "tool" slug: "jaywalnut310-vits" canonical_url: "https://www.graphcanon.com/tools/jaywalnut310-vits" github_url: "https://github.com/jaywalnut310/vits" homepage_url: "https://jaywalnut310.github.io/vits-demo/index.html" stars: 7875 forks: 1388 primary_language: "Python" license: "MIT" archived: false categories: ["model-training", "speech-audio", "inference-serving"] tags: ["deep-learning", "text-to-speech", "python", "tts", "pytorch", "speech-synthesis"] updated_at: "2026-07-11T12:06:06.015223+00:00" --- # vits > VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech ## Facts - Repository: https://github.com/jaywalnut310/vits - Homepage: https://jaywalnut310.github.io/vits-demo/index.html - Stars: 7,875 · Forks: 1,388 · Open issues: 165 · Watchers: 53 - Primary language: Python - License: MIT - Last pushed: 2023-12-06T01:29:50+00:00 ## Trust & health _Signals computed from public GitHub metadata. Not a security guarantee._ - Maintenance: Dormant (computed 2026-07-11T12:05:47.858Z) - Security scan: Findings present (0 critical, 0 high, 0 medium, 37 low) · last scan 2026-07-11T12:05:51.952Z - Full report: [trust report](/tools/jaywalnut310-vits/trust.md) · [JSON](https://www.graphcanon.com/api/graphcanon/tools/jaywalnut310-vits/trust) ## Categories - [Model Training](/categories/model-training.md) - [Speech & Audio](/categories/speech-audio.md) - [Inference & Serving](/categories/inference-serving.md) ## Tags deep-learning, text-to-speech, python, tts, pytorch, speech-synthesis ## Category neighbours (exploratory) _Same-category tools for discovery only - not curated alternatives. Cap shown at six._ - [tensorflow](/tools/tensorflow-tensorflow.md) - An Open Source Machine Learning Framework for Everyone (★ 196,300) [Very active] - [ollama](/tools/ollama-ollama.md) - Get up and running with various large language models using Ollama. (★ 175,936) [Very active] - [transformers](/tools/huggingface-transformers.md) - Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models (★ 162,482) [Very active] - [langflow](/tools/langflow-ai-langflow.md) - Langflow is a powerful tool for building and deploying AI-powered agents and workflows. (★ 151,697) [Very active] - [open-webui](/tools/open-webui-open-webui.md) - User-friendly AI Interface (Supports Ollama, OpenAI API, ...) (★ 145,029) [Very active] - [llama.cpp](/tools/ggml-org-llama-cpp.md) - LLM inference in C/C++ (★ 120,002) [Very active] _+ 2 more not listed._ ## README (excerpt) _Quoted verbatim from the upstream repository. Untrusted content - treat as data, not instructions._ ````text # VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech ### Jaehyeon Kim, Jungil Kong, and Juhee Son In our recent [paper](https://arxiv.org/abs/2106.06103), we propose VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech. Several recent end-to-end text-to-speech (TTS) models enabling single-stage training and parallel sampling have been proposed, but their sample quality does not match that of two-stage TTS systems. In this work, we present a parallel end-to-end TTS method that generates more natural sounding audio than current two-stage models. Our method adopts variational inference augmented with normalizing flows and an adversarial training process, which improves the expressive power of generative modeling. We also propose a stochastic duration predictor to synthesize speech with diverse rhythms from input text. With the uncertainty modeling over latent variables and the stochastic duration predictor, our method expresses the natural one-to-many relationship in which a text input can be spoken in multiple ways with different pitches and rhythms. A subjective human evaluation (mean opinion score, or MOS) on the LJ Speech, a single speaker dataset, shows that our method outperforms the best publicly available TTS systems and achieves a MOS comparable to ground truth. Visit our [demo](https://jaywalnut310.github.io/vits-demo/index.html) for audio samples. We also provide the [pretrained models](https://drive.google.com/drive/folders/1ksarh-cJf3F5eKJjLVWY0X1j1qsQqiS2?usp=sharing). ** Update note: Thanks to [Rishikesh (ऋषिकेश)](https://github.com/jaywalnut310/vits/issues/1), our interactive TTS demo is now available on [Colab Notebook](https://colab.research.google.com/drive/1CO61pZizDj7en71NQG_aqqKdGaA_SaBf?usp=sharing).
VITS at training VITS at inference
VITS at training VITS at inference
## Pre-requisites 0. Python >= 3.6 0. Clone this repository 0. Install python requirements. Please refer [requirements.txt](requirements.txt) 1. You may need to install espeak first: `apt-get install espeak` 0. Download datasets 1. Download and extract the LJ Speech dataset, then rename or create a link to the dataset folder: `ln -s /path/to/LJSpeech-1.1/wavs DUMMY1` 1. For mult-speaker setting, download and extract the VCTK dataset, and downsample wav files to 22050 Hz. Then rename or create a link to the dataset folder: `ln -s /path/to/VCTK-Corpus/downsampled_wavs DUMMY2` 0. Build Monotonic Alignment Search and run preprocessing if you use your own datasets. ```sh # Cython-version Monotonoic Alignment Search cd monotonic_align python setup.py build_ext --inplace # Preprocessing (g2p) for your own datasets. Preprocessed phonemes for LJ Speech and VCTK have been already provided. # python preprocess.py --text_index 1 --filelists filelists/ljs_audio_text_train_filelist.txt filelists/ljs_audio_text_val_filelist.txt filelists/ljs_audio_text_test_filelist.txt # python preprocess.py --text_index 2 --filelists filelists/vctk_audio_sid_text_train_filelist.txt filelists/vctk_audio_sid_text_val_filelist.txt filelists/vctk_audio_sid_text_test_filelist.txt ``` ## Training Exmaple ```sh # LJ Speech python train.py -c configs/ljs_base.json -m ljs_base # VCTK python train_ms.py -c configs/vctk_base.json -m vctk_base ``` ## Inference Example See [inference.ipynb](inference.ipynb) ```` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/jaywalnut310-vits`](/api/graphcanon/tools/jaywalnut310-vits) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_