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

Last update: Oct 28, 2022

Related tags

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

English: LJSpeech
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

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

Related tags

Overview

VAENAR-TTS

Samples | Paper | Pretrained Models

Usage

0. Dataset

1. Environment setup

2. Data pre-processing

3. Training

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

Reference

Owner

THUHCSI

Python wrapper class for OpenVINO Model Server. User can submit inference request to OVMS with just a few lines of code

A free, multiplatform SDK for real-time facial motion capture using blendshapes, and rigid head pose in 3D space from any RGB camera, photo, or video.

Code for Neurips2021 Paper "Topology-Imbalance Learning for Semi-Supervised Node Classification".

A object detecting neural network powered by the yolo architecture and leveraging the PyTorch framework and associated libraries.

Instant-Teaching: An End-to-End Semi-Supervised Object Detection Framework

Free-duolingo-plus - Duolingo account creator that uses your invite code to get you free duolingo plus

code for our BMVC 2021 paper "HCV: Hierarchy-Consistency Verification for Incremental Implicitly-Refined Classification"

Repository aimed at compiling code, papers, demos etc.. related to my PhD on 3D vision and machine learning for fruit detection and shape estimation at the university of Lincoln

PoseCamera is python based SDK for human pose estimation through RGB webcam.

Language Used: Python . Made in Jupyter(Anaconda) notebook.

ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees

Fast and scalable uncertainty quantification for neural molecular property prediction, accelerated optimization, and guided virtual screening.

Learning to See by Looking at Noise

Waymo motion prediction challenge 2021: 3rd place solution

This is a collection of simple PyTorch implementations of neural networks and related algorithms. These implementations are documented with explanations,

A forwarding MPI implementation that can use any other MPI implementation via an MPI ABI

Generalized hybrid model for mode-locked laser diodes with an extended passive cavity

Official implementation of "Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets" (CVPR2021)

Implementation of Hierarchical Transformer Memory (HTM) for Pytorch

这是一个deeplabv3-plus-pytorch的源码，可以用于训练自己的模型。