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Singing Voice Conversion based on Whisper & neural source-filter BigVGAN

GitHub Repo stars GitHub forks GitHub issues GitHub
Black technology based on the three giants of artificial intelligence:

OpenAI's whisper, 680,000 hours in multiple languages

Nvidia's bigvgan, anti-aliasing for speech generation

Microsoft's adapter, high-efficiency for fine-tuning

LoRA is not fully implemented in this project, but it can be found here: LoRA TTS & paper

use pretrain model to fine tune

lora-svc-baker.mp4

Dataset preparation

Necessary pre-processing:

  • 1 accompaniment separation, UVR
  • 2 cut audio, less than 30 seconds for whisper, slicer

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

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

Install dependencies

  • 1 software dependency

    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/

    Tip: whisper is built-in, do not install it additionally, it will conflict and report an error

  • 4 download pretrain model maxgan_pretrain_32K.pth, and do test

    python svc_inference.py --config configs/maxgan.yaml --model maxgan_pretrain_32K.pth --spk ./configs/singers/singer0001.npy --wave test.wav

Data preprocessing

use this command if you want to automate this:

python3 prepare/easyprocess.py

or step by step, as follows:

  • 1, re-sampling

    generate audio with a sampling rate of 16000Hz

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

    generate audio with a sampling rate of 32000Hz

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

  • 2, use 16K audio to extract pitch

    python prepare/preprocess_f0.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 timbre code

    python prepare/preprocess_speaker.py data_svc/waves-16k/ data_svc/speaker

  • 5, extract the singer code for inference

    python prepare/preprocess_speaker_ave.py data_svc/speaker/ data_svc/singer

  • 6, use 32k audio to generate training index

    python prepare/preprocess_train.py

  • 7, training file debugging

    python prepare/preprocess_zzz.py -c configs/maxgan.yaml

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: maxgan_pretrain_32K.pth

    set pretrain: "./maxgan_pretrain_32K.pth" in configs/maxgan.yaml,and adjust the learning rate appropriately, eg 1e-5

  • 1, start training

    python svc_trainer.py -c configs/maxgan.yaml -n svc

  • 2, resume training

    python svc_trainer.py -c configs/maxgan.yaml -n svc -p chkpt/svc/***.pth

  • 3, view log

    tensorboard --logdir logs/

final_model_loss

Inference

use this command if you want a GUI that does all the commands below:

python3 svc_gui.py

or step by step, as follows:

  • 1, export inference model

    python svc_export.py --config configs/maxgan.yaml --checkpoint_path chkpt/svc/***.pt

  • 2, 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

  • 3, extract the F0 parameter to the csv text format

    python pitch/inference.py -w test.wav -p test.csv

  • 4, specify parameters and infer

    python svc_inference.py --config configs/maxgan.yaml --model maxgan_g.pth --spk ./data_svc/singers/your_singer.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
  • 5, post by vad

    python svc_inference_post.py --ref test.wav --svc svc_out.wav --out svc_post.wav

Source of code and References

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

AdaSpeech: Adaptive Text to Speech for Custom Voice

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

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

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

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