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https://github.com/jaywalnut310/vits

VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech
https://github.com/jaywalnut310/vits

deep-learning pytorch speech-synthesis text-to-speech tts

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VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech

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# 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)