so-vits-svc-5.0-nine

<div align="center"> <h1> Variational Inference with adversarial learning for end-to-end Singing Voice Conversion based on VITS </h1>

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• 💗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

💗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

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

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/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

<a href="https://github.com/PlayVoice/so-vits-svc/graphs/contributors"> <img src="https://contrib.rocks/image?repo=PlayVoice/so-vits-svc" /> </a>

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license: afl-3.0