The official implementation of VAENAR-TTS, a VAE based non-autoregressive TTS model.

Related tags

Deep LearningVAENAR-TTS
Overview

VAENAR-TTS

This repo contains code accompanying the paper "VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis".

Samples | Paper | Pretrained Models

Usage

0. Dataset

  1. English: LJSpeech
  2. Mandarin: DataBaker(标贝)

1. Environment setup

conda env create -f environment.yml
conda activate vaenartts-env

2. Data pre-processing

For English using LJSpeech:

CUDA_VISIBLE_DEVICES= python preprocess.py --dataset ljspeech --data_dir /path/to/extracted/LJSpeech-1.1 --save_dir ./ljspeech

For Mandarin using Databaker(标贝):

CUDA_VISIBLE_DEVICES= python preprocess.py --dataset databaker --data_dir /path/to/extracted/biaobei --save_dir ./databaker

3. Training

For English using LJSpeech:

CUDA_VISIBLE_DEVICES=0 TF_FORCE_GPU_ALLOW_GROWTH=true python train.py --dataset ljspeech --log_dir ./lj-log_dir --test_dir ./lj-test_dir --data_dir ./ljspeech/tfrecords/ --model_dir ./lj-model_dir

For Mandarin using Databaker(标贝):

CUDA_VISIBLE_DEVICES=0 TF_FORCE_GPU_ALLOW_GROWTH=true python train.py --dataset databaker --log_dir ./db-log_dir --test_dir ./db-test_dir --data_dir ./databaker/tfrecords/ --model_dir ./db-model_dir

4. Inference (synthesize speech for the whole test set)

For English using LJSpeech:

CUDA_VISIBLE_DEVICES=0 TF_FORCE_GPU_ALLOW_GROWTH=true python inference.py --dataset ljspeech --test_dir ./lj-test-2000 --data_dir ./ljspeech/tfrecords/ --batch_size 16 --write_wavs true --draw_alignments true --ckpt_path ./lj-model_dir/ckpt-2000

For Mandarin using Databaker(标贝):

CUDA_VISIBLE_DEVICES=0 TF_FORCE_GPU_ALLOW_GROWTH=true python inference.py --dataset databaker --test_dir ./db-test-2000 --data_dir ./databaker/tfrecords/ --batch_size 16 --write_wavs true --draw_alignments true --ckpt_path ./db-model_dir/ckpt-2000

Reference

  1. XuezheMax/flowseq
  2. keithito/tacotron
Owner
THUHCSI
Human-Computer Speech Interaction Lab at Tsinghua University
THUHCSI
GitHub Repository
PiRank: Learning to Rank via Differentiable Sorting

PiRank: Learning to Rank via Differentiable Sorting This repository provides a reference implementation for learning PiRank-based models as described

54 Dec 17, 2022
Open-Ended Commonsense Reasoning (NAACL 2021)

Open-Ended Commonsense Reasoning Quick links: [Paper] | [Video] | [Slides] | [Documentation] This is the repository of the paper, Differentiable Open-

(Bill) Yuchen Lin 31 Oct 19, 2022
The Instructed Glacier Model (IGM)

The Instructed Glacier Model (IGM) Overview The Instructed Glacier Model (IGM) simulates the ice dynamics, surface mass balance, and its coupling thro

27 Dec 16, 2022
Churn-Prediction-Project - In this project, a churn prediction model is developed for a private bank as a term project for Data Mining class.

Churn-Prediction-Project In this project, a churn prediction model is developed for a private bank as a term project for Data Mining class. Project in

1 Jan 03, 2022
Code for our paper A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localization,

FSRA This repository contains the dataset link and the code for our paper A Transformer-Based Feature Segmentation and Region Alignment Method For UAV

Dmmm 32 Dec 18, 2022
It helps user to learn Pick-up lines and share if he has a better one

Pick-up-Lines-Generator(Open Source) It helps user to learn Pick-up lines Share and Add one or many to the DataBase Unique SQLite DataBase AI Undercon

knock_nott 0 May 04, 2022
:hot_pepper: R²SQL: "Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing." (AAAI 2021)

R²SQL The PyTorch implementation of paper Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing. (AAAI 2021) Requirement

huybery 60 Dec 31, 2022
SPEAR: Semi suPErvised dAta progRamming

Semi-Supervised Data Programming for Data Efficient Machine Learning SPEAR is a library for data programming with semi-supervision. The package implem

decile-team 91 Dec 06, 2022
Official PyTorch implementation of "Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble" (NeurIPS'21)

Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble This is the code for reproducing the results of the paper Uncertainty-Bas

43 Nov 23, 2022
Convolutional Neural Network for Text Classification in Tensorflow

This code belongs to the "Implementing a CNN for Text Classification in Tensorflow" blog post. It is slightly simplified implementation of Kim's Convo

Denny Britz 5.5k Jan 02, 2023
Implementation of "Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification"

hypergraph_reid Implementation of "Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification" If you find this help your research,

62 Dec 21, 2022
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.

Generative Models Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow. Also present here are RBM and Helmholtz Machine. Note: Gen

Agustinus Kristiadi 7k Jan 02, 2023
Deep Q-Learning Network in pytorch (not actively maintained)

pytoch-dqn This project is pytorch implementation of Human-level control through deep reinforcement learning and I also plan to implement the followin

Hung-Tu Chen 342 Jan 01, 2023
Benchmarks for semi-supervised domain generalization.

Semi-Supervised Domain Generalization This code is the official implementation of the following paper: Semi-Supervised Domain Generalization with Stoc

Kaiyang 49 Dec 10, 2022
DimReductionClustering - Dimensionality Reduction + Clustering + Unsupervised Score Metrics

Dimensionality Reduction + Clustering + Unsupervised Score Metrics Introduction

11 Nov 15, 2022
FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.

Detectron is deprecated. Please see detectron2, a ground-up rewrite of Detectron in PyTorch. Detectron Detectron is Facebook AI Research's software sy

Facebook Research 25.5k Jan 07, 2023
A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery

PiSL A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery. Sun, F., Liu, Y. and Sun, H., 2021. Physics-informe

Fangzheng (Andy) Sun 8 Jul 13, 2022
Practical Single-Image Super-Resolution Using Look-Up Table

Practical Single-Image Super-Resolution Using Look-Up Table [Paper] Dependency Python 3.6 PyTorch glob numpy pillow tqdm tensorboardx 1. Training deep

Younghyun Jo 116 Dec 23, 2022
Neural network for recognizing the gender of people in photos

Neural Network For Gender Recognition How to test it? Install requirements.txt file using pip install -r requirements.txt command Run nn.py using pyth

Valery Chapman 1 Sep 18, 2022
[CVPR 2021] Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion

[CVPR 2021] Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion

Rex Cheng 364 Jan 03, 2023