README_OLD.md

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

• This project targets deep learning beginners, basic knowledge of Python and PyTorch are the prerequisites for 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 practices;
• This project does not support real-time voice converting; (need to replace whisper if real-time voice converting is what you are looking for)
• This project will not develop one-click packages for other purposes;

• 6GB low minimum VRAM requirement for training

• support for multiple speakers

• create unique speakers through speaker mixing

• even voices with light accompaniment can also be converted

• F0 can be edited using Excel

https://github.com/PlayVoice/so-vits-svc-5.0/assets/16432329/6a09805e-ab93-47fe-9a14-9cbc1e0e7c3a

Powered by @ShadowVap

Model properties

Feature	From	Status	Function
whisper	OpenAI	✅	strong noise immunity
bigvgan	NVIDA	✅	alias and snake
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
SNAC	Samsung	✅	One Shot Clone of VITS
SCLN	Microsoft	✅	Improve Clone
PPG perturbation	this project	✅	Improved noise immunity and de-timbre
HuBERT perturbation	this project	✅	Improved noise immunity and de-timbre
VAE perturbation	this project	✅	Improve sound quality
MIX encoder	this project	✅	Improve conversion stability
USP infer	this project	✅	Improve conversion stability

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

USP : Unvoice and Silence with Pitch when infer

Quick Installation

# clone project
git clone https://github.com/ouor/so-vits-svc-5.0

# create virtual environment
python -m venv .venv

# activate virtual environment
.venv\Scripts\activate

# install pytorch
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117

# install dependencies
pip install -r requirements.txt

# run app.py
python app.py

Setup Environment

1. Install PyTorch.

2. Install project dependencies

   pip install -i https://pypi.tuna.tsinghua.edu.cn/simple -r requirements.txt
   

   Note: whisper is already built-in, do not install it again otherwise it will cuase conflict and error

3. Download the Timbre Encoder: Speaker-Encoder by @mueller91, put best_model.pth.tar into speaker_pretrain/.

4. Download whisper model whisper-large-v2. Make sure to download large-v2.pt，put it into whisper_pretrain/.

5. Download hubert_soft model，put hubert-soft-0d54a1f4.pt into hubert_pretrain/.

6. Download pitch extractor crepe full，put full.pth into crepe/assets.

7. Download pretrain model sovits5.0.pretrain.pth, and put it into vits_pretrain/.

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

Dataset preparation

Necessary pre-processing:

1. Separate vocie and accompaniment with UVR (skip if no accompaniment)
2. Cut audio input to shorter length with slicer, whisper takes input less than 30 seconds.
3. Manually check generated audio input, remove inputs shorter than 2 seconds or with obivous noise.
4. Adjust loudness if necessary, recommand Adobe Audiiton.
5. Put the dataset into the dataset_raw directory following the structure below.

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

Data preprocessing

python sve_preprocessing.py -t 2

-t: threading, max number should not exceed CPU core count, usually 2 is enough. After preprocessing you will get an output with following structure.

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
└── hubert
│    └── speaker0
│    │      ├── 000001.vec.npy
│    │      └── 000xxx.vec.npy
│    └── speaker1
│           ├── 000001.vec.npy
│           └── 000xxx.vec.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

1. Re-sampling

   • Generate audio with a sampling rate of 16000Hz in ./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 in ./data_svc/waves-32k

   python prepare/preprocess_a.py -w ./dataset_raw -o ./data_svc/waves-32k -s 32000
   

2. Use 16K audio to extract pitch

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

3. Use 16K audio to extract ppg

   python prepare/preprocess_ppg.py -w data_svc/waves-16k/ -p data_svc/whisper
   

4. Use 16K audio to extract hubert

   python prepare/preprocess_hubert.py -w data_svc/waves-16k/ -v data_svc/hubert
   

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
   

Train

1. If fine-tuning based on the pre-trained model, you need to download the pre-trained model: sovits5.0.pretrain.pth. Put pretrained model under project root, change this line

   pretrain: "./vits_pretrain/sovits5.0.pretrain.pth"
   

   in configs/base.yaml，and adjust the learning rate appropriately, eg 5e-5.

   batch_szie: for GPU with 6G VRAM, 6 is the recommended value, 8 will work but step speed will be much slower.

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. Log visualization

   tensorboard --logdir logs/
   

Inference

1. Export inference model: text encoder, Flow network, Decoder network

   python svc_export.py --config configs/base.yaml --checkpoint_path chkpt/sovits5.0/***.pt
   

2. Inference

   • if there is no need to adjust f0, just run the following command.

   python svc_inference.py --config configs/base.yaml --model sovits5.0.pth --spk ./data_svc/singer/your_singer.spk.npy --wave test.wav --shift 0
   

   • if f0 will be adjusted manually, follow the steps:
     1. use whisper to extract content encoding, generate test.vec.npy.

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

     2. use hubert to extract content vector, without using one-click reasoning, in order to reduce GPU memory usage

     python hubert/inference.py -w test.wav -v test.vec.npy
     

     3. 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
     

     4. final inference

     python svc_inference.py --config configs/base.yaml --model sovits5.0.pth --spk ./data_svc/singer/your_singer.spk.npy --wave test.wav --ppg test.ppg.npy --vec test.vec.npy --pit test.csv --shift 0
     

3. Notes

   • 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 --vec 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

4. Arguments ref

   args	--config	--model	--spk	--wave	--ppg	--vec	--pit	--shift
   name	config path	model path	speaker	wave input	wave ppg	wave hubert	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 will be eva.spk.npy.

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

Code sources and references

https://github.com/facebookresearch/speech-resynthesis paper

https://github.com/jaywalnut310/vits paper

https://github.com/openai/whisper/ paper

https://github.com/NVIDIA/BigVGAN paper

https://github.com/mindslab-ai/univnet paper

https://github.com/nii-yamagishilab/project-NN-Pytorch-scripts/tree/master/project/01-nsf

https://github.com/brentspell/hifi-gan-bwe

https://github.com/mozilla/TTS

https://github.com/bshall/soft-vc

https://github.com/maxrmorrison/torchcrepe

https://github.com/OlaWod/FreeVC paper

SNAC : Speaker-normalized Affine Coupling Layer in Flow-based Architecture for Zero-Shot Multi-Speaker Text-to-Speech

Adapter-Based Extension of Multi-Speaker Text-to-Speech Model for New Speakers

AdaSpeech: Adaptive Text to Speech for Custom Voice

Cross-Speaker Prosody Transfer on Any Text for Expressive Speech Synthesis

Learn to Sing by Listening: Building Controllable Virtual Singer by Unsupervised Learning from Voice Recordings

Adversarial Speaker Disentanglement Using Unannotated External Data for Self-supervised Representation Based Voice Conversion

Speaker normalization (GRL) for self-supervised speech emotion recognition

Method of Preventing Timbre Leakage Based on Data Perturbation

https://github.com/auspicious3000/contentvec/blob/main/contentvec/data/audio/audio_utils_1.py

https://github.com/revsic/torch-nansy/blob/main/utils/augment/praat.py

https://github.com/revsic/torch-nansy/blob/main/utils/augment/peq.py

https://github.com/biggytruck/SpeechSplit2/blob/main/utils.py

https://github.com/OlaWod/FreeVC/blob/main/preprocess_sr.py

Contributors

Relevant Projects

• LoRA-SVC: decoder only svc
• NSF-BigVGAN: vocoder for more work