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/
CoReNet is a technique for joint multi-object 3D reconstruction from a single RGB image.

CoReNet CoReNet is a technique for joint multi-object 3D reconstruction from a single RGB image. It produces coherent reconstructions, where all objec

Google Research 80 Dec 25, 2022
The repo of the preprinting paper "Labels Are Not Perfect: Inferring Spatial Uncertainty in Object Detection"

Inferring Spatial Uncertainty in Object Detection A teaser version of the code for the paper Labels Are Not Perfect: Inferring Spatial Uncertainty in

ZINING WANG 21 Mar 03, 2022
iris - Open Source Photos Platform Powered by PyTorch

Open Source Photos Platform Powered by PyTorch. Submission for PyTorch Annual Hackathon 2021.

Omkar Prabhu 137 Sep 10, 2022
Official PyTorch implementation of "AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks"

AASIST This repository provides the overall framework for training and evaluating audio anti-spoofing systems proposed in 'AASIST: Audio Anti-Spoofing

Clova AI Research 56 Jan 02, 2023
[ICCV21] Code for RetrievalFuse: Neural 3D Scene Reconstruction with a Database

RetrievalFuse Paper | Project Page | Video RetrievalFuse: Neural 3D Scene Reconstruction with a Database Yawar Siddiqui, Justus Thies, Fangchang Ma, Q

Yawar Nihal Siddiqui 75 Dec 22, 2022
Code for "LoFTR: Detector-Free Local Feature Matching with Transformers", CVPR 2021

LoFTR: Detector-Free Local Feature Matching with Transformers Project Page | Paper LoFTR: Detector-Free Local Feature Matching with Transformers Jiami

ZJU3DV 1.4k Jan 04, 2023
RITA is a family of autoregressive protein models, developed by LightOn in collaboration with the OATML group at Oxford and the Debora Marks Lab at Harvard.

RITA: a Study on Scaling Up Generative Protein Sequence Models RITA is a family of autoregressive protein models, developed by a collaboration of Ligh

LightOn 69 Dec 22, 2022
True per-item rarity for Loot

True-Rarity True per-item rarity for Loot (For Adventurers) and More Loot A.K.A mLoot each out/true_rarity_{item_type}.json file contains probabilitie

Dan R. 3 Jul 26, 2022
Official code for article "Expression is enough: Improving traffic signal control with advanced traffic state representation"

1 Introduction Official code for article "Expression is enough: Improving traffic signal control with advanced traffic state representation". The code s

Liang Zhang 10 Dec 10, 2022
PyTorch implementation of our CVPR2021 (oral) paper "Prototype Augmentation and Self-Supervision for Incremental Learning"

PASS - Official PyTorch Implementation [CVPR2021 Oral] Prototype Augmentation and Self-Supervision for Incremental Learning Fei Zhu, Xu-Yao Zhang, Chu

67 Dec 27, 2022
Aligning Latent and Image Spaces to Connect the Unconnectable

About This repo contains the official implementation of the Aligning Latent and Image Spaces to Connect the Unconnectable paper. It is a GAN model whi

Ivan Skorokhodov 203 Jan 03, 2023
A cross-lingual COVID-19 fake news dataset

CrossFake An English-Chinese COVID-19 fake&real news dataset from the ICDMW 2021 paper below: Cross-lingual COVID-19 Fake News Detection. Jiangshu Du,

Yingtong Dou 11 Dec 01, 2022
🎁 3,000,000+ Unsplash images made available for research and machine learning

The Unsplash Dataset The Unsplash Dataset is made up of over 250,000+ contributing global photographers and data sourced from hundreds of millions of

Unsplash 2k Jan 03, 2023
Learning Intents behind Interactions with Knowledge Graph for Recommendation, WWW2021

Learning Intents behind Interactions with Knowledge Graph for Recommendation This is our PyTorch implementation for the paper: Xiang Wang, Tinglin Hua

158 Dec 15, 2022
Official implementation for paper: A Latent Transformer for Disentangled Face Editing in Images and Videos.

A Latent Transformer for Disentangled Face Editing in Images and Videos Official implementation for paper: A Latent Transformer for Disentangled Face

InterDigital 108 Dec 09, 2022
A PyTorch implementation of the architecture of Mask RCNN

EDIT (AS OF 4th NOVEMBER 2019): This implementation has multiple errors and as of the date 4th, November 2019 is insufficient to be utilized as a reso

Sai Himal Allu 975 Dec 30, 2022
Blender Add-On for slicing meshes with planes

MeshSlicer Blender Add-On for slicing meshes with multiple overlapping planes at once. This is a simple Blender addon to slice a silmple mesh with mul

52 Dec 12, 2022
Object tracking implemented with YOLOv4, DeepSort, and TensorFlow.

Object tracking implemented with YOLOv4, DeepSort, and TensorFlow. YOLOv4 is a state of the art algorithm that uses deep convolutional neural networks to perform object detections. We can take the ou

The AI Guy 1.1k Dec 29, 2022
PanopticBEV - Bird's-Eye-View Panoptic Segmentation Using Monocular Frontal View Images

Bird's-Eye-View Panoptic Segmentation Using Monocular Frontal View Images This r

63 Dec 16, 2022
PySOT - SenseTime Research platform for single object tracking, implementing algorithms like SiamRPN and SiamMask.

PySOT is a software system designed by SenseTime Video Intelligence Research team. It implements state-of-the-art single object tracking algorit

STVIR 4.1k Dec 29, 2022