An 16kHz implementation of HiFi-GAN for soft-vc.

Related tags

Deep Learningspeech-synthesisvoice-conversionpytorch-implementation
Overview

HiFi-GAN

An 16kHz implementation of HiFi-GAN for soft-vc.

Relevant links:

Example Usage

import torch
import numpy as np

# Load checkpoint
hifigan = torch.hub.load("bshall/hifigan:main", "hifigan_hubert_soft").cuda()
# Load mel-spectrogram
mel = torch.from_numpy(np.load("path/to/mel")).unsqueeze(0).cuda()
# Generate
wav, sr = hifigan.generate(mel)

Train

Step 1: Download and extract the LJ-Speech dataset

Step 2: Resample the audio to 16kHz:

usage: resample.py [-h] [--sample-rate SAMPLE_RATE] in-dir out-dir

Resample an audio dataset.

positional arguments:
  in-dir                path to the dataset directory
  out-dir               path to the output directory

optional arguments:
  -h, --help            show this help message and exit
  --sample-rate SAMPLE_RATE
                        target sample rate (default 16kHz)

Step 3: Download the dataset splits and move them into the root of the dataset directory. After steps 2 and 3 your dataset directory should look like this:

LJSpeech-1.1
│   test.txt
│   train.txt
│   validation.txt
├───mels
└───wavs

Note: the mels directory is optional. If you want to fine-tune HiFi-GAN the mels directory should contain ground-truth aligned spectrograms from an acoustic model.

Step 4: Train HiFi-GAN:

usage: train.py [-h] [--resume RESUME] [--finetune] dataset-dir checkpoint-dir

Train or finetune HiFi-GAN.

positional arguments:
  dataset-dir      path to the preprocessed data directory
  checkpoint-dir   path to the checkpoint directory

optional arguments:
  -h, --help       show this help message and exit
  --resume RESUME  path to the checkpoint to resume from
  --finetune       whether to finetune (note that a resume path must be given)

Generate

To generate using the trained HiFi-GAN models, see Example Usage or use the generate.py script:

usage: generate.py [-h] [--model-name {hifigan,hifigan-hubert-soft,hifigan-hubert-discrete}] in-dir out-dir

Generate audio for a directory of mel-spectrogams using HiFi-GAN.

positional arguments:
  in-dir                path to directory containing the mel-spectrograms
  out-dir               path to output directory

optional arguments:
  -h, --help            show this help message and exit
  --model-name {hifigan,hifigan-hubert-soft,hifigan-hubert-discrete}
                        available models

Acknowledgements

This repo is based heavily on https://github.com/jik876/hifi-gan.

You might also like...
Fast Soft Color Segmentation
Fast Soft Color Segmentation

Fast Soft Color Segmentation

null 3 Oct 29, 2022
Permute Me Softly: Learning Soft Permutations for Graph Representations

Permute Me Softly: Learning Soft Permutations for Graph Representations

Giannis Nikolentzos 7 Jul 10, 2022
Multi-task Multi-agent Soft Actor Critic for SMAC

Multi-task Multi-agent Soft Actor Critic for SMAC Overview The CARE formulti-task: Multi-Task Reinforcement Learning with Context-based Representation

RuanJingqing 8 Sep 30, 2022
[ICLR 2022] Contact Points Discovery for Soft-Body Manipulations with Differentiable Physics
[ICLR 2022] Contact Points Discovery for Soft-Body Manipulations with Differentiable Physics

CPDeform Code and data for paper Contact Points Discovery for Soft-Body Manipulations with Differentiable Physics at ICLR 2022 (Spotlight). @InProceed

(Lester) Sizhe Li 29 Nov 29, 2022
Implementation of 'lightweight' GAN, proposed in ICLR 2021, in Pytorch. High resolution image generations that can be trained within a day or two
Implementation of 'lightweight' GAN, proposed in ICLR 2021, in Pytorch. High resolution image generations that can be trained within a day or two

512x512 flowers after 12 hours of training, 1 gpu 256x256 flowers after 12 hours of training, 1 gpu Pizza 'Lightweight' GAN Implementation of 'lightwe

Phil Wang 1.5k Jan 2, 2023
Implementation of TransGanFormer, an all-attention GAN that combines the finding from the recent GanFormer and TransGan paper

TransGanFormer (wip) Implementation of TransGanFormer, an all-attention GAN that combines the finding from the recent GansFormer and TransGan paper. I

Phil Wang 146 Dec 6, 2022
PyTorch 1.5 implementation for paper DECOR-GAN: 3D Shape Detailization by Conditional Refinement.
PyTorch 1.5 implementation for paper DECOR-GAN: 3D Shape Detailization by Conditional Refinement.

DECOR-GAN PyTorch 1.5 implementation for paper DECOR-GAN: 3D Shape Detailization by Conditional Refinement, Zhiqin Chen, Vladimir G. Kim, Matthew Fish

Zhiqin Chen 72 Dec 31, 2022
This is a pytorch implementation of the NeurIPS paper GAN Memory with No Forgetting.

GAN Memory for Lifelong learning This is a pytorch implementation of the NeurIPS paper GAN Memory with No Forgetting. Please consider citing our paper

Miaoyun Zhao 43 Dec 27, 2022
[CVPR 2021] Pytorch implementation of Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs

Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs In this work, we propose a framework HijackGAN, which enables non-linear latent space travers

Hui-Po Wang 46 Sep 5, 2022
Comments
  • is pretrained weight of discriminator of base model available?

    is pretrained weight of discriminator of base model available?

    Thanks for nice work. @bshall

    I'm trying to train hifigan now, but it takes so long training it from scratch using other dataset.

    If discriminator of base model is also available, I could start finetuning based on that vocoder. it seems that you released only generator. Could you also release discriminator weights?

    opened by seastar105 3
  • NaN during training when using own dataset

    NaN during training when using own dataset

    While fine-tuning works as expected, doing regular training with a dataset that isn't LJSpeech would eventually cause a NaN loss at some point. The culprit appears to be the following line, which causes a division by zero if wav happens to contain perfect silence:

    https://github.com/bshall/hifigan/blob/374a4569eae5437e2c80d27790ff6fede9fc1c46/hifigan/dataset.py#L106

    I'm not sure what the best solution for this would be, as a quick fix I simply clipped the divisor so it can't reach zero:

    wav = flip * gain * wav / max([wav.abs().max(), 0.001])
    opened by cjay42 0
  • How to use this Vocoder with your Tacotron?

    How to use this Vocoder with your Tacotron?

    Thank you for your work. I used your Tacotron in your Universal Vocoding.The quality of the speech is excellent. However, the inference speed is slow. for that reason, I would like to use this hifigan as a vocoder. But Tacotron's n_mel is 80, while hifigan's n_mel is 128. How to use hifigan with Tacotron?

    opened by gheyret 0
Owner
Benjamin van Niekerk
PhD student at Stellenbosch University. Interested in speech and audio technology.
Benjamin van Niekerk
GitHub Repository https://bshall.github.io/soft-vc/
RATCHET is a Medical Transformer for Chest X-ray Diagnosis and Reporting

RATCHET: RAdiological Text Captioning for Human Examined Thoraxes RATCHET is a Medical Transformer for Chest X-ray Diagnosis and Reporting. Based on t

26 Nov 14, 2022
Pytorch Lightning Distributed Accelerators using Ray

Distributed PyTorch Lightning Training on Ray This library adds new PyTorch Lightning plugins for distributed training using the Ray distributed compu

167 Jan 02, 2023
NeurIPS-2021: Neural Auto-Curricula in Two-Player Zero-Sum Games.

NAC Official PyTorch implementation of NAC from the paper: Neural Auto-Curricula in Two-Player Zero-Sum Games. We release code for: Gradient based ora

Xidong Feng 19 Nov 11, 2022
PyTorch implementation of the ExORL: Exploratory Data for Offline Reinforcement Learning

ExORL: Exploratory Data for Offline Reinforcement Learning This is an original PyTorch implementation of the ExORL framework from Don't Change the Alg

Denis Yarats 52 Jan 01, 2023
Get a Grip! - A robotic system for remote clinical environments.

Get a Grip! Within clinical environments, sterilization is an essential procedure for disinfecting surgical and medical instruments. For our engineeri

Jay Sharma 1 Jan 05, 2022
Iterative Training: Finding Binary Weight Deep Neural Networks with Layer Binarization

Iterative Training: Finding Binary Weight Deep Neural Networks with Layer Binarization This repository contains the source code for the paper (link wi

Rakuten Group, Inc. 0 Nov 19, 2021
Python scripts performing class agnostic object localization using the Object Localization Network model in ONNX.

ONNX Object Localization Network Python scripts performing class agnostic object localization using the Object Localization Network model in ONNX. Ori

Ibai Gorordo 15 Oct 14, 2022
Codes for NeurIPS 2021 paper "Adversarial Neuron Pruning Purifies Backdoored Deep Models"

Adversarial Neuron Pruning Purifies Backdoored Deep Models Code for NeurIPS 2021 "Adversarial Neuron Pruning Purifies Backdoored Deep Models" by Dongx

Dongxian Wu 31 Dec 11, 2022
Code for Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation (CVPR 2021)

Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation (CVPR 2021) Hang Zhou, Yasheng Sun, Wayne Wu, Chen Cha

Hang_Zhou 628 Dec 28, 2022
Improving Object Detection by Estimating Bounding Box Quality Accurately

Improving Object Detection by Estimating Bounding Box Quality Accurately Abstrac

2 Apr 14, 2022
Video Autoencoder: self-supervised disentanglement of 3D structure and motion

Video Autoencoder: self-supervised disentanglement of 3D structure and motion This repository contains the code (in PyTorch) for the model introduced

157 Dec 22, 2022
FAMIE is a comprehensive and efficient active learning (AL) toolkit for multilingual information extraction (IE)

FAMIE: A Fast Active Learning Framework for Multilingual Information Extraction

18 Sep 01, 2022
Block-wisely Supervised Neural Architecture Search with Knowledge Distillation (CVPR 2020)

DNA This repository provides the code of our paper: Blockwisely Supervised Neural Architecture Search with Knowledge Distillation. Illustration of DNA

Changlin Li 215 Dec 19, 2022
FID calculation with proper image resizing and quantization steps

clean-fid: Fixing Inconsistencies in FID Project | Paper The FID calculation involves many steps that can produce inconsistencies in the final metric.

Gaurav Parmar 606 Jan 06, 2023
A project for developing transformer-based models for clinical relation extraction

Clinical Relation Extration with Transformers Aim This package is developed for researchers easily to use state-of-the-art transformers models for ext

uf-hobi-informatics-lab 101 Dec 19, 2022
Official implementation of Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble Models at NeurIPS 2021

Representer Point Selection via Local Jacobian Expansion for Classifier Explanation of Deep Neural Networks and Ensemble Models This repository is the

Yi(Amy) Sui 2 Dec 01, 2021
Source code for our CVPR 2019 paper - PPGNet: Learning Point-Pair Graph for Line Segment Detection

PPGNet: Learning Point-Pair Graph for Line Segment Detection PyTorch implementation of our CVPR 2019 paper: PPGNet: Learning Point-Pair Graph for Line

SVIP Lab 170 Oct 25, 2022
Turning SymPy expressions into PyTorch modules.

sympytorch A micro-library as a convenience for turning SymPy expressions into PyTorch Modules. All SymPy floats become trainable parameters. All SymP

Patrick Kidger 89 Dec 13, 2022
Semi-supervised learning for object detection

Source code for STAC: A Simple Semi-Supervised Learning Framework for Object Detection STAC is a simple yet effective SSL framework for visual object

Google Research 348 Dec 25, 2022
Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch

30 Days Of Machine Learning Using Pytorch Objective of the repository is to learn and build machine learning models using Pytorch. List of Algorithms

Mayur 119 Nov 24, 2022