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

Related tags

Deep Learningvits
Overview

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, 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 for audio samples.

We also provide the pretrained models.

VITS at training VITS at inference
VITS at training VITS at inference

Pre-requisites

  1. Python >= 3.6
  2. Clone this repository
  3. Install python requirements. Please refer requirements.txt
    1. You may need to install espeak first: apt-get install espeak
  4. 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
    2. 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
  5. Build Monotonic Alignment Search and run preprocessing if you use your own datasets.
# 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

# 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

Owner
Jaehyeon Kim
Jaehyeon Kim
PyTorch implementation of "Representing Shape Collections with Alignment-Aware Linear Models" paper.

deep-linear-shapes PyTorch implementation of "Representing Shape Collections with Alignment-Aware Linear Models" paper. If you find this code useful i

Romain Loiseau 27 Sep 24, 2022
A faster pytorch implementation of faster r-cnn

A Faster Pytorch Implementation of Faster R-CNN Write at the beginning [05/29/2020] This repo was initaited about two years ago, developed as the firs

Jianwei Yang 7.1k Jan 01, 2023
Code for the paper "Next Generation Reservoir Computing"

Next Generation Reservoir Computing This is the code for the results and figures in our paper "Next Generation Reservoir Computing". They are written

OSU QuantInfo Lab 105 Dec 20, 2022
"Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback"

This is code repo for our EMNLP 2017 paper "Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback", which implements the A2C algorithm on top of a neural encoder-

Khanh Nguyen 131 Oct 21, 2022
Sharpened cosine similarity torch - A Sharpened Cosine Similarity layer for PyTorch

Sharpened Cosine Similarity A layer implementation for PyTorch Install At your c

Brandon Rohrer 203 Nov 30, 2022
Official implementation of the paper "AAVAE: Augmentation-AugmentedVariational Autoencoders"

AAVAE Official implementation of the paper "AAVAE: Augmentation-AugmentedVariational Autoencoders" Abstract Recent methods for self-supervised learnin

Grid AI Labs 48 Dec 12, 2022
The original weights of some Caffe models, ported to PyTorch.

pytorch-caffe-models This repo contains the original weights of some Caffe models, ported to PyTorch. Currently there are: GoogLeNet (Going Deeper wit

Katherine Crowson 9 Nov 04, 2022
Official PyTorch Implementation of paper EAN: Event Adaptive Network for Efficient Action Recognition

Official PyTorch Implementation of paper EAN: Event Adaptive Network for Efficient Action Recognition

TianYuan 27 Nov 07, 2022
This is the code of NeurIPS'21 paper "Towards Enabling Meta-Learning from Target Models".

ST This is the code of NeurIPS 2021 paper "Towards Enabling Meta-Learning from Target Models". If you use any content of this repo for your work, plea

Su Lu 7 Dec 06, 2022
Making self-supervised learning work on molecules by using their 3D geometry to pre-train GNNs. Implemented in DGL and Pytorch Geometric.

3D Infomax improves GNNs for Molecular Property Prediction Video | Paper We pre-train GNNs to understand the geometry of molecules given only their 2D

Hannes Stärk 95 Dec 30, 2022
[CoRL 2021] A robotics benchmark for cross-embodiment imitation.

x-magical x-magical is a benchmark extension of MAGICAL specifically geared towards cross-embodiment imitation. The tasks still provide the Demo/Test

Kevin Zakka 36 Nov 26, 2022
Implementation of trRosetta and trDesign for Pytorch, made into a convenient package

trRosetta - Pytorch (wip) Implementation of trRosetta and trDesign for Pytorch, made into a convenient package

Phil Wang 67 Dec 17, 2022
True Few-Shot Learning with Language Models

This codebase supports using language models (LMs) for true few-shot learning: learning to perform a task using a limited number of examples from a single task distribution.

Ethan Perez 124 Jan 04, 2023
Python and Julia in harmony.

PythonCall & JuliaCall Bringing Python® and Julia together in seamless harmony: Call Python code from Julia and Julia code from Python via a symmetric

Christopher Rowley 414 Jan 07, 2023
This repository contains pre-trained models and some evaluation code for our paper Towards Unsupervised Dense Information Retrieval with Contrastive Learning

Contriever: Towards Unsupervised Dense Information Retrieval with Contrastive Learning This repository contains pre-trained models and some evaluation

Meta Research 207 Jan 08, 2023
Portfolio asset allocation strategies: from Markowitz to RNNs

Portfolio asset allocation strategies: from Markowitz to RNNs Research project to explore different approaches for optimal portfolio allocation starti

Luigi Filippo Chiara 1 Feb 05, 2022
This repository contains the source code of an efficient 1D probabilistic model for music time analysis proposed in ICASSP2022 venue.

Jump Reward Inference for 1D Music Rhythmic State Spaces An implementation of the probablistic jump reward inference model for music rhythmic informat

Mojtaba Heydari 25 Dec 16, 2022
Official PyTorch Implementation of Convolutional Hough Matching Networks, CVPR 2021 (oral)

Convolutional Hough Matching Networks This is the implementation of the paper "Convolutional Hough Matching Network" by J. Min and M. Cho. Implemented

Juhong Min 70 Nov 22, 2022
Code for reproducing our paper: LMSOC: An Approach for Socially Sensitive Pretraining

LMSOC: An Approach for Socially Sensitive Pretraining Code for reproducing the paper LMSOC: An Approach for Socially Sensitive Pretraining to appear a

Twitter Research 11 Dec 20, 2022
Official pytorch implementation of Active Learning for deep object detection via probabilistic modeling (ICCV 2021)

Active Learning for Deep Object Detection via Probabilistic Modeling This repository is the official PyTorch implementation of Active Learning for Dee

NVIDIA Research Projects 130 Jan 06, 2023