readme: ---
  pipeline_tag: text-to-speech
  tags:
  - ' Text-to-Speech'
  - audio
  widget:
  - text: "你好，你在做什么呀?"
  ---
  # 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).
  
  <table style="width:100%">
    <tr>
      <th>VITS at training</th>
      <th>VITS at inference</th>
    </tr>
    <tr>
      <td><img src="resources/fig_1a.png" alt="VITS at training" height="400"></td>
      <td><img src="resources/fig_1b.png" alt="VITS at inference" height="400"></td>
    </tr>
  </table>
  
  
  ## 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)