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https://github.com/MoonInTheRiver/NeuralSVB

Learning the Beauty in Songs: Neural Singing Voice Beautifier; ACL 2022 (Main conference); Official code
https://github.com/MoonInTheRiver/NeuralSVB

acl2022 gan singing-synthesis singing-voice singing-voice-conversion singing-voice-synthesis

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Learning the Beauty in Songs: Neural Singing Voice Beautifier; ACL 2022 (Main conference); Official code

Host: GitHub
URL: https://github.com/MoonInTheRiver/NeuralSVB
Owner: MoonInTheRiver
License: gpl-3.0
Created: 2022-03-01T07:28:19.000Z (almost 3 years ago)
Default Branch: master
Last Pushed: 2024-01-02T08:30:32.000Z (12 months ago)
Last Synced: 2024-08-02T11:24:39.817Z (5 months ago)
Topics: acl2022, gan, singing-synthesis, singing-voice, singing-voice-conversion, singing-voice-synthesis
Language: Python
Homepage:
Size: 1.95 MB
Stars: 416
Watchers: 13
Forks: 51
Open Issues: 14
Metadata Files:
- Readme: README.md
- License: LICENSE

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README

# Learning the Beauty in Songs: Neural Singing Voice Beautifier
---
[![arXiv](https://img.shields.io/badge/arXiv-Paper-.svg)](https://arxiv.org/abs/2202.13277)
[![GitHub Stars](https://img.shields.io/github/stars/MoonInTheRiver/NeuralSVB)](https://github.com/MoonInTheRiver/NeuralSVB)
![visitors](https://visitor-badge.glitch.me/badge?page_id=moonintheriver/NeuralSVB)

Demo Page

This repository is the official PyTorch implementation of our ACL-2022 [paper](https://arxiv.org/abs/2202.13277).

## 0. Dataset (PopBuTFy) Acquirement
### Audio samples
- You can download the dataset from [here](https://drive.google.com/file/d/1IKFp7y1WeYGrwXgJ0HC3rdPj54WoqIsU/view?usp=sharing). Please send us an email for registration (See in [apply_form](resources/apply_form.md)).
- Dataset [preview](https://github.com/MoonInTheRiver/NeuralSVB/releases/download/pre-release/PopBuTFy-preview.zip).

### Text labels
NeuralSVB does not need text as input, but the ASR model to extract PPG needs text. Thus we also provide the [text labels](https://github.com/MoonInTheRiver/NeuralSVB/releases/download/pre-release/text_labels.zip) of PopBuTFy.

## 1. Preparation

### Environment Preparation
Most of the required packages are in https://github.com/NATSpeech/NATSpeech/blob/main/requirements.txt

Or you can prepare environments with the Requirements.txt file in the repository directory.
```sh
pip install Requirements.txt
```
### Data Preparation

1. Extract embeddings of vocal timbre:
```sh
CUDA_VISIBLE_DEVICES=0 python data_gen/tts/bin/binarize.py --config egs/datasets/audio/PopBuTFy/save_emb.yaml
```
2. Pack the dataset:
```sh
CUDA_VISIBLE_DEVICES=0 python data_gen/tts/bin/binarize.py --config egs/datasets/audio/PopBuTFy/para_bin.yaml
```

### Vocoder Preparation
We provide the pre-trained model of [HifiGAN-Singing](https://github.com/MoonInTheRiver/NeuralSVB/releases/download/pre-release/1012_hifigan_all_songs_nsf.zip) which is specially designed for SVS with NSF mechanism.

Please unzip pre-trained vocoder into `checkpoints` before training your acoustic model.

This singing vocoder is trained on 100+ hours singing data (including Chinese and English songs).

### PPG Extractor Preparation
We provide the pre-trained model of [PPG Extractor](https://github.com/MoonInTheRiver/NeuralSVB/releases/download/pre-release/1009_pretrain_asr_english.zip).

Please unzip pre-trained PPG extractor into `checkpoints` before training your acoustic model.

After the instructions above, the directory structure should be as follows:

## 2. Training Example

```sh
CUDA_VISIBLE_DEVICES=0,1 python tasks/run.py --config egs/datasets/audio/PopBuTFy/vae_global_mle_eng.yaml --exp_name exp_name --reset
```

## 3. Inference
### Inference from packed test set

```sh
CUDA_VISIBLE_DEVICES=0,1 python tasks/run.py --config egs/datasets/audio/PopBuTFy/vae_global_mle_eng.yaml --exp_name exp_name --reset --infer
```
Inference results will be saved in `./checkpoints/EXP_NAME/generated_` by default.

We provided:
- the [pre-trained model](https://github.com/MoonInTheRiver/NeuralSVB/releases/download/pre-release/1030_vae_mle.zip) of NSVB (en version);

Remember to put the pre-trained models in `checkpoints` directory.

### Inference from raw inputs
WIP.

## Limitations
See Appendix D "Limitations and Solutions" in our [paper](https://aclanthology.org/2022.acl-long.549.pdf).

## Citation
If this repository helps your research, please cite:

@inproceedings{liu-etal-2022-learning-beauty,
title = "Learning the Beauty in Songs: Neural Singing Voice Beautifier",
author = "Liu, Jinglin and
Li, Chengxi and
Ren, Yi and
Zhu, Zhiying and
Zhao, Zhou",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.549",
pages = "7970--7983",}

## Issues
- Before raising a issue, please check our Readme and other issues for possible solutions.
- We will try to handle your problem in time but we could not guarantee a satisfying solution.
- Please be friendly.

## Acknowledgements
* r9y9's [wavenet_vocoder](https://github.com/r9y9/wavenet_vocoder)
* Po-Hsun-Su's [ssim](https://github.com/Po-Hsun-Su/pytorch-ssim)
* descriptinc's [melgan](https://github.com/descriptinc/melgan-neurips)
* Official [espnet](https://github.com/espnet/espnet)
* Official [PyTorch Lightning](https://github.com/PyTorchLightning/pytorch-lightning)

The framework of this repository is based on [DiffSinger](https://github.com/MoonInTheRiver/DiffSinger),
and is a predecessor of [NATSpeech](https://github.com/NATSpeech/NATSpeech/).