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

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
A framework for analyzing computer vision models with simulated data

3DB: A framework for analyzing computer vision models with simulated data Paper Quickstart guide Blog post Installation Follow instructions on: https:

3DB 112 Jan 01, 2023
Understanding and Overcoming the Challenges of Efficient Transformer Quantization

Transformer Quantization This repository contains the implementation and experiments for the paper presented in Yelysei Bondarenko1, Markus Nagel1, Ti

83 Dec 30, 2022
Point Cloud Registration using Representative Overlapping Points.

Point Cloud Registration using Representative Overlapping Points (ROPNet) Abstract 3D point cloud registration is a fundamental task in robotics and c

ZhuLifa 36 Dec 16, 2022
Prososdy Morph: A python library for manipulating pitch and duration in an algorithmic way, for resynthesizing speech.

ProMo (Prosody Morph) Questions? Comments? Feedback? Chat with us on gitter! A library for manipulating pitch and duration in an algorithmic way, for

Tim 71 Jan 02, 2023
Neural Scene Graphs for Dynamic Scene (CVPR 2021)

Implementation of Neural Scene Graphs, that optimizes multiple radiance fields to represent different objects and a static scene background. Learned representations can be rendered with novel object

151 Dec 26, 2022
The repo of Feedback Networks, CVPR17

Feedback Networks http://feedbacknet.stanford.edu/ Paper: Feedback Networks, CVPR 2017. Amir R. Zamir*,Te-Lin Wu*, Lin Sun, William B. Shen, Bertram E

Stanford Vision and Learning Lab 87 Nov 19, 2022
On Effective Scheduling of Model-based Reinforcement Learning

On Effective Scheduling of Model-based Reinforcement Learning Code to reproduce the experiments in On Effective Scheduling of Model-based Reinforcemen

laihang 8 Oct 07, 2022
CSAC - Collaborative Semantic Aggregation and Calibration for Separated Domain Generalization

CSAC Introduction This repository contains the implementation code for paper: Co

ScottYuan 5 Jul 22, 2022
Code for the paper "Curriculum Dropout", ICCV 2017

Curriculum Dropout Dropout is a very effective way of regularizing neural networks. Stochastically "dropping out" units with a certain probability dis

Pietro Morerio 21 Jan 02, 2022
Train/evaluate a Keras model, get metrics streamed to a dashboard in your browser.

Hera Train/evaluate a Keras model, get metrics streamed to a dashboard in your browser. Setting up Step 1. Plant the spy Install the package pip

Keplr 495 Dec 10, 2022
Pseudo-rng-app - whos needs science to make a random number when you have pseudoscience?

Pseudo-random numbers with pseudoscience rng is so complicated! Why cant we have a horoscopic, vibe-y way of calculating a random number? Why cant rng

Andrew Blance 1 Dec 27, 2021
Neural Logic Inductive Learning

Neural Logic Inductive Learning This is the implementation of the Neural Logic Inductive Learning model (NLIL) proposed in the ICLR 2020 paper: Learn

36 Nov 28, 2022
PyTorch implementation of the paper: "Preference-Adaptive Meta-Learning for Cold-Start Recommendation", IJCAI, 2021.

PAML PyTorch implementation of the paper: "Preference-Adaptive Meta-Learning for Cold-Start Recommendation", IJCAI, 2021. (Continuously updating ) Int

15 Nov 18, 2022
YouRefIt: Embodied Reference Understanding with Language and Gesture

YouRefIt: Embodied Reference Understanding with Language and Gesture YouRefIt: Embodied Reference Understanding with Language and Gesture by Yixin Che

16 Jul 11, 2022
Package to compute Mauve, a similarity score between neural text and human text. Install with `pip install mauve-text`.

MAUVE MAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE

Krishna Pillutla 182 Jan 02, 2023
ReAct: Out-of-distribution Detection With Rectified Activations

ReAct: Out-of-distribution Detection With Rectified Activations This is the source code for paper ReAct: Out-of-distribution Detection With Rectified

38 Dec 05, 2022
Simulating an AI playing 2048 using the Expectimax algorithm

2048-expectimax Simulating an AI playing 2048 using the Expectimax algorithm The base game engine uses code from here. The AI player is modeled as a m

Subha Ramesh 2 Jan 31, 2022
Implement of "Training deep neural networks via direct loss minimization" in PyTorch for 0-1 loss

This is the implementation of "Training deep neural networks via direct loss minimization" published at ICML 2016 in PyTorch. The implementation targe

Cuong Nguyen 1 Jan 18, 2022
Running AlphaFold2 (from ColabFold) in Azure Machine Learning

Running AlphaFold2 (from ColabFold) in Azure Machine Learning Colby T. Ford, Ph.D. Companion repository for Medium Post: How to predict many protein s

Colby T. Ford 3 Feb 18, 2022
Code for the Lovász-Softmax loss (CVPR 2018)

The Lovász-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks Maxim Berman, Amal Ranne

Maxim Berman 1.3k Jan 04, 2023