Variational Inference with adversarial learning for end-to-end Singing Voice Conversion based on VITS

GitHub Repo stars GitHub forks GitHub issues GitHub

💗This project is target for: beginners in deep learning, the basic operation of Python and PyTorch is the prerequisite for using this project;
💗This project aims to help deep learning beginners get rid of boring pure theoretical learning, and master the basic knowledge of deep learning by combining it with practice;
💗This project does not support real-time voice change; (support needs to replace whisper)
💗This project will not develop one-click packages for other purposes；

6G memory GPU can be used to trained
support for multiple speakers
create unique speakers through speaker mixing
even with light accompaniment can also be converted
F0 can be edited using Excel

Model properties

https://github.com/PlayVoice/so-vits-svc-5.0/releases/tag/hifigan_release

sovits5.0_main_1500.pth The model includes: generator + discriminator = 176M, which can be used as a pre-training model
speakers files are in the configs/singers directory, which can be used for reasoning tests, especially for timbre leakage
speakers 22, 30, 47, and 51 are highly recognizable, and the training audio samples are in the configs/singers_sample directory

Feature	From	Status	Function	Remarks
whisper	OpenAI	✅	strong noise immunity	-
bigvgan	NVIDA	✅	alias and snake	The GPU takes up a little more, and the main branch is deleted; You need to switch to the branch bigvgan，the formant is clearer and the sound quality is obviously improved
natural speech	Microsoft	✅	reduce mispronunciation	-
neural source-filter	NII	✅	solve the problem of audio F0 discontinuity	-
speaker encoder	Google	✅	Timbre Encoding and Clustering	-
GRL for speaker	Ubisoft	✅	Preventing Encoder Leakage Timbre	-
one shot vits	Samsung	✅	Voice Clone	-
SCLN	Microsoft	✅	Improve Clone	-
PPG perturbation	this project	✅	Improved noise immunity and de-timbre	-
VAE perturbation	this project	✅	Improve sound quality	-

💗due to the use of data perturbation, it takes longer to train than other projects.

Dataset preparation

Necessary pre-processing:

1 accompaniment separation
2 band extension
3 sound quality improvement
4 cut audio, less than 30 seconds for whisper💗

then put the dataset into the dataset_raw directory according to the following file structure

dataset_raw
├───speaker0
│   ├───000001.wav
│   ├───...
│   └───000xxx.wav
└───speaker1
    ├───000001.wav
    ├───...
    └───000xxx.wav

Install dependencies

1 software dependency

apt update && sudo apt install ffmpeg

pip install -r requirements.txt
2 download the Timbre Encoder: Speaker-Encoder by @mueller91, put best_model.pth.tar into speaker_pretrain/
3 download whisper model multiple language medium model, Make sure to download medium.pt，put it into whisper_pretrain/
4 whisper is built-in, do not install it additionally, it will conflict and report an error

Data preprocessing

1， set working directory:

export PYTHONPATH=$PWD
2， re-sampling

generate audio with a sampling rate of 16000Hz：./data_svc/waves-16k

python prepare/preprocess_a.py -w ./dataset_raw -o ./data_svc/waves-16k -s 16000

generate audio with a sampling rate of 32000Hz：./data_svc/waves-32k

python prepare/preprocess_a.py -w ./dataset_raw -o ./data_svc/waves-32k -s 32000
3， use 16K audio to extract pitch：f0_ceil=900, it needs to be modified according to the highest pitch of your data

python prepare/preprocess_f0.py -w data_svc/waves-16k/ -p data_svc/pitch

or use next for low quality audio

python prepare/preprocess_f0_crepe.py -w data_svc/waves-16k/ -p data_svc/pitch
4， use 16K audio to extract ppg

python prepare/preprocess_ppg.py -w data_svc/waves-16k/ -p data_svc/whisper
5， use 16k audio to extract timbre code

python prepare/preprocess_speaker.py data_svc/waves-16k/ data_svc/speaker
6， extract the average value of the timbre code for inference; it can also replace a single audio timbre in generating the training index, and use it as the unified timbre of the speaker for training

python prepare/preprocess_speaker_ave.py data_svc/speaker/ data_svc/singer
7， use 32k audio to extract the linear spectrum

python prepare/preprocess_spec.py -w data_svc/waves-32k/ -s data_svc/specs
8， use 32k audio to generate training index

python prepare/preprocess_train.py
9， training file debugging

python prepare/preprocess_zzz.py

data_svc/
└── waves-16k
│    └── speaker0
│    │      ├── 000001.wav
│    │      └── 000xxx.wav
│    └── speaker1
│           ├── 000001.wav
│           └── 000xxx.wav
└── waves-32k
│    └── speaker0
│    │      ├── 000001.wav
│    │      └── 000xxx.wav
│    └── speaker1
│           ├── 000001.wav
│           └── 000xxx.wav
└── pitch
│    └── speaker0
│    │      ├── 000001.pit.npy
│    │      └── 000xxx.pit.npy
│    └── speaker1
│           ├── 000001.pit.npy
│           └── 000xxx.pit.npy
└── whisper
│    └── speaker0
│    │      ├── 000001.ppg.npy
│    │      └── 000xxx.ppg.npy
│    └── speaker1
│           ├── 000001.ppg.npy
│           └── 000xxx.ppg.npy
└── speaker
│    └── speaker0
│    │      ├── 000001.spk.npy
│    │      └── 000xxx.spk.npy
│    └── speaker1
│           ├── 000001.spk.npy
│           └── 000xxx.spk.npy
└── singer
    ├── speaker0.spk.npy
    └── speaker1.spk.npy

Train

0， if fine-tuning based on the pre-trained model, you need to download the pre-trained model: sovits5.0_main_1500.pth

set pretrain: "./sovits5.0_main_1500.pth" in configs/base.yaml，and adjust the learning rate appropriately, eg 1e-5
1， set working directory

export PYTHONPATH=$PWD
2， start training

python svc_trainer.py -c configs/base.yaml -n sovits5.0
3， resume training

python svc_trainer.py -c configs/base.yaml -n sovits5.0 -p chkpt/sovits5.0/***.pth
4， view log

tensorboard --logdir logs/

Inference

1， set working directory

export PYTHONPATH=$PWD
2， export inference model: text encoder, Flow network, Decoder network

python svc_export.py --config configs/base.yaml --checkpoint_path chkpt/sovits5.0/***.pt
3， use whisper to extract content encoding, without using one-click reasoning, in order to reduce GPU memory usage

python whisper/inference.py -w test.wav -p test.ppg.npy

generate test.ppg.npy; if no ppg file is specified in the next step, generate it automatically
4， extract the F0 parameter to the csv text format, open the csv file in Excel, and manually modify the wrong F0 according to Audition or SonicVisualiser

python pitch/inference.py -w test.wav -p test.csv
5，specify parameters and infer

python svc_inference.py --config configs/base.yaml --model sovits5.0.pth --spk ./configs/singers/singer0001.npy --wave test.wav --ppg test.ppg.npy --pit test.csv

when --ppg is specified, when the same audio is reasoned multiple times, it can avoid repeated extraction of audio content codes; if it is not specified, it will be automatically extracted;

when --pit is specified, the manually tuned F0 parameter can be loaded; if not specified, it will be automatically extracted;

generate files in the current directory:svc_out.wav

args --config --model --spk --wave --ppg --pit --shift

name config path model path speaker wave input wave ppg wave pitch pitch shift

args	--config	--model	--spk	--wave	--ppg	--pit	--shift
name	config path	model path	speaker	wave input	wave ppg	wave pitch	pitch shift

Creat singer

named by pure coincidence：average -> ave -> eva，eve(eva) represents conception and reproduction

python svc_eva.py

eva_conf = {
    './configs/singers/singer0022.npy': 0,
    './configs/singers/singer0030.npy': 0,
    './configs/singers/singer0047.npy': 0.5,
    './configs/singers/singer0051.npy': 0.5,
}

the generated singer file is：eva.spk.npy

💗both Flow and Decoder need to input timbres, and you can even input different timbre parameters to the two modules to create more unique timbres.

Data set

Name	URL
KiSing	http://shijt.site/index.php/2021/05/16/kising-the-first-open-source-mandarin-singing-voice-synthesis-corpus/
PopCS	https://github.com/MoonInTheRiver/DiffSinger/blob/master/resources/apply_form.md
opencpop	https://wenet.org.cn/opencpop/download/
Multi-Singer	https://github.com/Multi-Singer/Multi-Singer.github.io
M4Singer	https://github.com/M4Singer/M4Singer/blob/master/apply_form.md
CSD	https://zenodo.org/record/4785016#.YxqrTbaOMU4
KSS	https://www.kaggle.com/datasets/bryanpark/korean-single-speaker-speech-dataset
JVS MuSic	https://sites.google.com/site/shinnosuketakamichi/research-topics/jvs_music
PJS	https://sites.google.com/site/shinnosuketakamichi/research-topics/pjs_corpus
JUST Song	https://sites.google.com/site/shinnosuketakamichi/publication/jsut-song
MUSDB18	https://sigsep.github.io/datasets/musdb.html#musdb18-compressed-stems
DSD100	https://sigsep.github.io/datasets/dsd100.html
Aishell-3	http://www.aishelltech.com/aishell_3
VCTK	https://datashare.ed.ac.uk/handle/10283/2651