FG-transformer-TTS Fine-grained style control in transformer-based text-to-speech synthesis

Related tags

Deep LearningFG-transformer-TTS
Overview

LST-TTS

Official implementation for the paper Fine-grained style control in transformer-based text-to-speech synthesis. Submitted to ICASSP 2022. Audio samples/demo for our system can be accessed here

Setting up submodules

git submodule update --init --recursive

Get the waveglow vocoder checkpoint from here (This is from the NVIDIA official WaveGlow repo).

Setup environment

See docker/Dockerfile for the packages need to be installed.

Dataset preprocessing

LJSpeech

python preprocess_LJSpeech.py --datadir LJSpeechDir --outputdir OutputDir

VCTK

Get the leading and trailing scilence marks from this repo, and put vctk-silences.0.92.txt in your VCTK dataset directory.

python preprocess_VCTK.py --datadir VCTKDir --outputdir Output_Train_Dir

python preprocess_VCTK.py --datadir VCTKDir --outputdir Output_Test_Dir --make_test_set
  • --make_test_set: specify this flag to process the speakers in the test set, otherwise only process training speakers.

Training

LJSpeech

python train_TTS.py --precision 16 \
                    --datadir FeatureDir \
                    --vocoder_ckpt_path WaveGlowCKPT_PATH \
                    --sampledir SampleDir \
                    --batch_size 128 \
                    --check_val_every_n_epoch 50 \
                    --use_guided_attn \
                    --training_step 250000 \
                    --n_guided_steps 250000 \
                    --saving_path Output_CKPT_DIR \
                    --datatype LJSpeech \
                    [--distributed]
  • --distributed: enable DDP multi-GPU training
  • --batch_size: batch size per GPU, scale down if you train with multi-GPU and want to keep the same batch size
  • --check_val_every_n_epoch: sample and validate every n epoch
  • --datadir: output directory of the preprocess scripts

VCTK

python train_TTS.py --precision 16 \
                    --datadir FeatureDir \
                    --vocoder_ckpt_path WaveGlowCKPT_PATH \
                    --sampledir SampleDir \
                    --batch_size 64 \
                    --check_val_every_n_epoch 50 \
                    --use_guided_attn \
                    --training_step 150000 \
                    --n_guided_steps 150000 \
                    --etts_checkpoint LJSpeech_Model_CKPT \
                    --saving_path Output_CKPT_DIR \
                    --datatype VCTK \
                    [--distributed]
  • --etts_checkpoint: the checkpoint path of pretrained model (on LJ Speech)

Synthesis

We provide examples for synthesis of the system in synthesis.py, you can adjust this script to your own usage. Example to run synthesis.py:

python synthesis.py --etts_checkpoint VCTK_Model_CKPT \
                    --sampledir SampleDir \
                    --datatype VCTK \
                    --vocoder_ckpt_path WaveGlowCKPT_PATH
Owner
Li-Wei Chen
Li-Wei Chen
GitHub Repository
Pytorch Implementation of Auto-Compressing Subset Pruning for Semantic Image Segmentation

Pytorch Implementation of Auto-Compressing Subset Pruning for Semantic Image Segmentation Introduction ACoSP is an online pruning algorithm that compr

Merantix 8 Dec 07, 2022
Improving Query Representations for DenseRetrieval with Pseudo Relevance Feedback:A Reproducibility Study.

APR The repo for the paper Improving Query Representations for DenseRetrieval with Pseudo Relevance Feedback:A Reproducibility Study. Environment setu

ielab 8 Nov 26, 2022
Classification of EEG data using Deep Learning

Graduation-Project Classification of EEG data using Deep Learning Epilepsy is the most common neurological disease in the world. Epilepsy occurs as a

Osman Alpaydın 5 Jun 24, 2022
Generating Radiology Reports via Memory-driven Transformer

R2Gen This is the implementation of Generating Radiology Reports via Memory-driven Transformer at EMNLP-2020. Citations If you use or extend our work,

CUHK-SZ NLP Group 101 Dec 13, 2022
An API-first distributed deployment system of deep learning models using timeseries data to analyze and predict systems behaviour

Gordo Building thousands of models with timeseries data to monitor systems. Table of content About Examples Install Uninstall Developer manual How to

Equinor 26 Dec 27, 2022
Out of Distribution Detection on Natural Adversarial Examples

OOD-on-NAE Research project on out of distribution detection for the Computer Vision course by Prof. Rob Fergus (CSCI-GA 2271) Paper out on arXiv - ht

Anugya 1 Jun 08, 2022
Full-featured Decision Trees and Random Forests learner.

CID3 This is a full-featured Decision Trees and Random Forests learner. It can save trees or forests to disk for later use. It is possible to query tr

Alejandro Penate-Diaz 3 Aug 15, 2022
Companion repo of the UCC 2021 paper "Predictive Auto-scaling with OpenStack Monasca"

Predictive Auto-scaling with OpenStack Monasca Giacomo Lanciano*, Filippo Galli, Tommaso Cucinotta, Davide Bacciu, Andrea Passarella 2021 IEEE/ACM 14t

Giacomo Lanciano 0 Dec 07, 2022
Knowledge Management for Humans using Machine Learning & Tags

HyperTag HyperTag helps humans intuitively express how they think about their files using tags and machine learning.

Ravn Tech, Inc. 165 Nov 04, 2022
Angular & Electron desktop UI framework. Angular components for native looking and behaving macOS desktop UI (Electron/Web)

Angular Desktop UI This is a collection for native desktop like user interface components in Angular, especially useful for Electron apps. It starts w

Marc J. Schmidt 49 Dec 22, 2022
Generating retro pixel game characters with Generative Adversarial Networks. Dataset "TinyHero" included.

pixel_character_generator Generating retro pixel game characters with Generative Adversarial Networks. Dataset "TinyHero" included. Dataset TinyHero D

Agnieszka Mikołajczyk 88 Nov 17, 2022
Advbox is a toolbox to generate adversarial examples that fool neural networks in PaddlePaddle、PyTorch、Caffe2、MxNet、Keras、TensorFlow and Advbox can benchmark the robustness of machine learning models.

Advbox is a toolbox to generate adversarial examples that fool neural networks in PaddlePaddle、PyTorch、Caffe2、MxNet、Keras、TensorFlow and Advbox can benchmark the robustness of machine learning models

AdvBox 1.3k Dec 25, 2022
The repository contains source code and models to use PixelNet architecture used for various pixel-level tasks. More details can be accessed at .

PixelNet: Representation of the pixels, by the pixels, and for the pixels. We explore design principles for general pixel-level prediction problems, f

Aayush Bansal 196 Aug 10, 2022
Price-Prediction-For-a-Dream-Home - A machine learning based linear regression trained model for house price prediction.

Price-Prediction-For-a-Dream-Home ROADMAP TO THIS LINEAR REGRESSION BASED HOUSE PRICE PREDICTION PREDICTION MODEL Import all the dependencies of the p

DIKSHA DESWAL 1 Dec 29, 2021
Python implementation of 3D facial mesh exaggeration using the techniques described in the paper: Computational Caricaturization of Surfaces.

Python implementation of 3D facial mesh exaggeration using the techniques described in the paper: Computational Caricaturization of Surfaces.

Wonjong Jang 8 Nov 01, 2022
Generative Handwriting using LSTM Mixture Density Network with TensorFlow

Generative Handwriting Demo using TensorFlow An attempt to implement the random handwriting generation portion of Alex Graves' paper. See my blog post

hardmaru 686 Nov 24, 2022
A Pytree Module system for Deep Learning in JAX

Treex A Pytree-based Module system for Deep Learning in JAX Intuitive: Modules are simple Python objects that respect Object-Oriented semantics and sh

Cristian Garcia 216 Dec 20, 2022
OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark

Introduction English | 简体中文 MMAction2 is an open-source toolbox for video understanding based on PyTorch. It is a part of the OpenMMLab project. The m

OpenMMLab 2.7k Jan 07, 2023
Segmentation vgg16 fcn - cityscapes

VGGSegmentation Segmentation vgg16 fcn - cityscapes Priprema skupa skripta prepare_dataset_downsampled.py Iz slika cityscapesa izrezuje haubu automobi

6 Oct 24, 2020
⚖️🔁🔮🕵️‍♂️🦹🖼️ Code for *Measuring the Contribution of Multiple Model Representations in Detecting Adversarial Instances* paper.

Measuring the Contribution of Multiple Model Representations in Detecting Adversarial Instances This repository contains the code for Measuring the Co

Daniel Steinberg 0 Nov 06, 2022