This is a template for the Non-autoregressive Deep Learning-Based TTS model (in PyTorch).

Related tags

Deep Learningtemplatetext-to-speechdeep-learningpytorchttsspeech-synthesisneural-ttsnon-autoregressivefastspeech
Overview

Non-autoregressive Deep Learning-Based TTS Template

This is a template for the Non-autoregressive TTS model. It contains

  • Data Preprocessing Pipeline
  • Data Loader
  • Model / Trainer
  • Logger, Postprocessing (logging, synthesizing, plotting, etc..)

How to use it?

  1. Clone the repository. 
    git clone https://github.com/keonlee9420/Deep-Learning-TTS-Template
cd Deep-Learning-TTS-Template
  2. Replace all MYMODEL strings in this repo with your model name and also rename the file model/MYMODEL.py.
  3. Build your model on model/ and check train.py and synthesize.py.
  4. Use README_template.md for the README.md file of your project.
  5. Feel free to add /img for your model architecture and tensorboard examples. It would also be nice to show your model's output audio in /demo.
  6. Don't forget to update requirements.txt and /config of your project.

Citation

@misc{lee2021deep_learning_tts_template,
  author = {Lee, Keon},
  title = {Deep-Learning-TTS-Template},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/keonlee9420/Deep-Learning-TTS-Template}}
}

References

You might also like...
Pytorch Implementation of DiffSinger: Diffusion Acoustic Model for Singing Voice Synthesis (TTS Extension)
Pytorch Implementation of DiffSinger: Diffusion Acoustic Model for Singing Voice Synthesis (TTS Extension)

DiffSinger - PyTorch Implementation PyTorch implementation of DiffSinger: Diffusion Acoustic Model for Singing Voice Synthesis (TTS Extension). Status

Keon Lee 152 Jan 2, 2023
Pytorch implementation of "Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech"

GradTTS Unofficial Pytorch implementation of "Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech" (arxiv) About this repo This is an unoffic

HeyangXue1997 103 Dec 23, 2022
A non-linear, non-parametric Machine Learning method capable of modeling complex datasets
A non-linear, non-parametric Machine Learning method capable of modeling complex datasets

Fast Symbolic Regression Symbolic Regression is a non-linear, non-parametric Machine Learning method capable of modeling complex data sets. fastsr aim

VAMSHI CHOWDARY 3 Jun 22, 2022
A Robust Non-IoU Alternative to Non-Maxima Suppression in Object Detection
A Robust Non-IoU Alternative to Non-Maxima Suppression in Object Detection

Confluence: A Robust Non-IoU Alternative to Non-Maxima Suppression in Object Detection 1. 介绍 用以替代 NMS，在所有 bbox 中挑选出最优的集合。 NMS 仅考虑了 bbox 的得分，然后根据 IOU 来

null 44 Sep 15, 2022
This project uses Template Matching technique for object detecting by detection of template image over base image.
This project uses Template Matching technique for object detecting by detection of template image over base image.

Object Detection Project Using OpenCV This project uses Template Matching technique for object detecting by detection the template image over base ima

Pratham Bhatnagar 7 May 29, 2022
This project uses Template Matching technique for object detecting by detection of template image over base image
This project uses Template Matching technique for object detecting by detection of template image over base image

Object Detection Project Using OpenCV This project uses Template Matching technique for object detecting by detection the template image over base ima

Pratham Bhatnagar 4 Nov 16, 2021
Curvlearn, a Tensorflow based non-Euclidean deep learning framework.
Curvlearn, a Tensorflow based non-Euclidean deep learning framework.

English | 简体中文 Why Non-Euclidean Geometry Considering these simple graph structures shown below. Nodes with same color has 2-hop distance whereas 1-ho

Alibaba 123 Dec 12, 2022
ESGD-M - A stochastic non-convex second order optimizer, suitable for training deep learning models, for PyTorch

ESGD-M - A stochastic non-convex second order optimizer, suitable for training deep learning models, for PyTorch

Katherine Crowson 53 Dec 29, 2022
Byte-based multilingual transformer TTS for low-resource/few-shot language adaptation.

One model to speak them all 🌎 Audio Language Text ▷ Chinese 人人生而自由，在尊严和权利上一律平等。 ▷ English All human beings are born free and equal in dignity and rig

Mutian He 60 Nov 14, 2022
Releases(v1.0.0)
Owner
Keon Lee
Expressive Speech Synthesis | Conversational AI | Open-domain Dialog | NLP | Generative Models | Empathic Computing | HCI
Keon Lee
GitHub Repository
Colab notebook for openai/glide-text2im.

GLIDE text2im on Colab This repository provides a Colab notebook to produce images conditioned on text prompts with GLIDE [1]. Usage Run text2im.ipynb

Wok 19 Oct 19, 2022
Neural-Pull: Learning Signed Distance Functions from Point Clouds by Learning to Pull Space onto Surfaces(ICML 2021)

Neural-Pull: Learning Signed Distance Functions from Point Clouds by Learning to Pull Space onto Surfaces(ICML 2021) This repository contains the code

149 Dec 15, 2022
Stock-history-display - something like a easy yearly review for your stock performance

Stock History Display Available on Heroku: https://stock-history-display.herokua

LiaoJJ 1 Jan 07, 2022
Depth image based mouse cursor visual haptic

Depth image based mouse cursor visual haptic How to run it. Install pyqt5. Install python modules pip install Pillow pip install numpy For illustrati

Xiong Jie 17 Dec 20, 2022
A Fast and Accurate One-Stage Approach to Visual Grounding, ICCV 2019 (Oral)

One-Stage Visual Grounding ***** New: Our recent work on One-stage VG is available at ReSC.***** A Fast and Accurate One-Stage Approach to Visual Grou

Zhengyuan Yang 118 Dec 05, 2022
Keras implementation of Normalizer-Free Networks and SGD - Adaptive Gradient Clipping

Keras implementation of Normalizer-Free Networks and SGD - Adaptive Gradient Clipping

Yam Peleg 63 Sep 21, 2022
Source code for paper "Deep Superpixel-based Network for Blind Image Quality Assessment"

DSN-IQA Source code for paper "Deep Superpixel-based Network for Blind Image Quality Assessment" Requirements Python =3.8.0 Pytorch =1.7.1 Usage wit

7 Oct 13, 2022
PyTorch code for JEREX: Joint Entity-Level Relation Extractor

JEREX: "Joint Entity-Level Relation Extractor" PyTorch code for JEREX: "Joint Entity-Level Relation Extractor". For a description of the model and exp

LAVIS - NLP Working Group 50 Dec 01, 2022
PolyphonicFormer: Unified Query Learning for Depth-aware Video Panoptic Segmentation

PolyphonicFormer: Unified Query Learning for Depth-aware Video Panoptic Segmentation Winner method of the ICCV-2021 SemKITTI-DVPS Challenge. [arxiv] [

Yuan Haobo 38 Jan 03, 2023
PyTorch implementation of CloudWalk's recent work DenseBody

densebody_pytorch PyTorch implementation of CloudWalk's recent paper DenseBody. Note: For most recent updates, please check out the dev branch. Update

Lingbo Yang 401 Nov 19, 2022
An Open-Source Tool for Automatic Disease Diagnosis..

OpenMedicalChatbox An Open-Source Package for Automatic Disease Diagnosis. Overview Due to the lack of open source for existing RL-base automated diag

8 Nov 08, 2022
PyTorch implementation of Memory-based semantic segmentation for off-road unstructured natural environments.

MemSeg: Memory-based semantic segmentation for off-road unstructured natural environments Introduction This repository is a PyTorch implementation of

11 Nov 28, 2022
TAPEX: Table Pre-training via Learning a Neural SQL Executor

TAPEX: Table Pre-training via Learning a Neural SQL Executor The official repository which contains the code and pre-trained models for our paper TAPE

Microsoft 157 Dec 28, 2022
Pansharpening by convolutional neural networks in the full resolution framework

Z-PNN: Zoom Pansharpening Neural Network Pansharpening by convolutional neural networks in the full resolution framework is a deep learning method for

20 Nov 24, 2022
This is the official code of L2G, Unrolling and Recurrent Unrolling in Learning to Learn Graph Topologies.

Learning to Learn Graph Topologies This is the official code of L2G, Unrolling and Recurrent Unrolling in Learning to Learn Graph Topologies. Requirem

Stacy X PU 16 Dec 09, 2022
Codes of paper "Unseen Object Amodal Instance Segmentation via Hierarchical Occlusion Modeling"

Unseen Object Amodal Instance Segmentation (UOAIS) Seunghyeok Back, Joosoon Lee, Taewon Kim, Sangjun Noh, Raeyoung Kang, Seongho Bak, Kyoobin Lee This

GIST-AILAB 92 Dec 13, 2022
PyTorch implementation of DUL (Data Uncertainty Learning in Face Recognition, CVPR2020)

PyTorch implementation of DUL (Data Uncertainty Learning in Face Recognition, CVPR2020)

Mouxiao Huang 20 Nov 15, 2022
TimeSHAP explains Recurrent Neural Network predictions.

TimeSHAP TimeSHAP is a model-agnostic, recurrent explainer that builds upon KernelSHAP and extends it to the sequential domain. TimeSHAP computes even

Feedzai 90 Dec 18, 2022
🕹️ Official Implementation of Conditional Motion In-betweening (CMIB) 🏃

Conditional Motion In-Betweening (CMIB) Official implementation of paper: Conditional Motion In-betweeening. Paper(arXiv) | Project Page | YouTube in-

Jihoon Kim 81 Dec 22, 2022
A GOOD REPRESENTATION DETECTS NOISY LABELS

A GOOD REPRESENTATION DETECTS NOISY LABELS This code is a PyTorch implementation of the paper: Prerequisites Python 3.6.9 PyTorch 1.7.1 Torchvision 0.

<a href=[email protected]"> 64 Jan 04, 2023