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/
Unofficial Implementation of MLP-Mixer in TensorFlow

mlp-mixer-tf Unofficial Implementation of MLP-Mixer [abs, pdf] in TensorFlow. Note: This project may have some bugs in it. I'm still learning how to i

Rishabh Anand 24 Mar 23, 2022
One Million Scenes for Autonomous Driving

ONCE Benchmark This is a reproduced benchmark for 3D object detection on the ONCE (One Million Scenes) dataset. The code is mainly based on OpenPCDet.

148 Dec 28, 2022
A variational Bayesian method for similarity learning in non-rigid image registration (CVPR 2022)

A variational Bayesian method for similarity learning in non-rigid image registration We provide the source code and the trained models used in the re

daniel grzech 14 Nov 21, 2022
Code for the paper Language as a Cognitive Tool to Imagine Goals in Curiosity Driven Exploration

IMAGINE: Language as a Cognitive Tool to Imagine Goals in Curiosity Driven Exploration This repo contains the code base of the paper Language as a Cog

Flowers Team 26 Dec 22, 2022
Space Ship Simulator using python

FlyOver Basic space-ship simulator using python How to run? Just double click run.py What modules do i need? All modules that i currently using is bui

0 Oct 09, 2022
Disturbing Target Values for Neural Network regularization: attacking the loss layer to prevent overfitting

Disturbing Target Values for Neural Network regularization: attacking the loss layer to prevent overfitting 1. Classification Task PyTorch implementat

Yongho Kim 0 Apr 24, 2022
Blind visual quality assessment on 360° Video based on progressive learning

Blind visual quality assessment on omnidirectional or 360 video (ProVQA) Blind VQA for 360° Video via Progressively Learning from Pixels, Frames and V

5 Jan 06, 2023
The MLOps platform for innovators 🚀

​ DS2.ai is an integrated AI operation solution that supports all stages from custom AI development to deployment. It is an AI-specialized platform service that collects data, builds a training datas

9 Jan 03, 2023
[ICCV 2021] Focal Frequency Loss for Image Reconstruction and Synthesis

Focal Frequency Loss - Official PyTorch Implementation This repository provides the official PyTorch implementation for the following paper: Focal Fre

Liming Jiang 460 Jan 04, 2023
This repository contains all data used for writing a research paper Multiple Object Trackers in OpenCV: A Benchmark, presented in ISIE 2021 conference in Kyoto, Japan.

OpenCV-Multiple-Object-Tracking Python is version 3.6.7 to install opencv: pip uninstall opecv-python pip uninstall opencv-contrib-python pip install

6 Dec 19, 2021
This is the offical website for paper ''Category-consistent deep network learning for accurate vehicle logo recognition''

The Pytorch Implementation of Category-consistent deep network learning for accurate vehicle logo recognition This is the offical website for paper ''

Wanglong Lu 28 Oct 29, 2022
Line-level Handwritten Text Recognition (HTR) system implemented with TensorFlow.

Line-level Handwritten Text Recognition with TensorFlow This model is an extended version of the Simple HTR system implemented by @Harald Scheidl and

Hoàng Tùng Lâm (Linus) 72 May 07, 2022
Implementation of the paper NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series Forecasting.

Non-AR Spatial-Temporal Transformer Introduction Implementation of the paper NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series For

Chen Kai 66 Nov 28, 2022
Official implementation of particle-based models (GNS and DPI-Net) on the Physion dataset.

Physion: Evaluating Physical Prediction from Vision in Humans and Machines [paper] Daniel M. Bear, Elias Wang, Damian Mrowca, Felix J. Binder, Hsiao-Y

Hsiao-Yu Fish Tung 18 Dec 19, 2022
Image processing in Python

scikit-image: Image processing in Python Website (including documentation): https://scikit-image.org/ Mailing list: https://mail.python.org/mailman3/l

Image Processing Toolbox for SciPy 5.2k Dec 31, 2022
OpenDILab RL Kubernetes Custom Resource and Operator Lib

DI Orchestrator DI Orchestrator is designed to manage DI (Decision Intelligence) jobs using Kubernetes Custom Resource and Operator. Prerequisites A w

OpenDILab 205 Dec 29, 2022
Data Augmentation Using Keras and Python

Data-Augmentation-Using-Keras-and-Python Data augmentation is the process of increasing the number of training dataset. Keras library offers a simple

Happy N. Monday 3 Feb 15, 2022
JORLDY an open-source Reinforcement Learning (RL) framework provided by KakaoEnterprise

Repository for Open Source Reinforcement Learning Framework JORLDY

Kakao Enterprise Corp. 330 Dec 30, 2022
This repository implements and evaluates convolutional networks on the Möbius strip as toy model instantiations of Coordinate Independent Convolutional Networks.

Orientation independent Möbius CNNs This repository implements and evaluates convolutional networks on the Möbius strip as toy model instantiations of

Maurice Weiler 59 Dec 09, 2022
BBScan py3 - BBScan py3 With Python

BBScan_py3 This repository is forked from lijiejie/BBScan 1.5. I migrated the fo

baiyunfei 12 Dec 30, 2022