Neural Source-Filter BigVGAN

Just For Fun

Dataset preparation

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 pretrained nsf_bigvgan_pretrain_32K.pth, and test

  python nsf_bigvgan_inference.py --config configs/nsf_bigvgan.yaml --model nsf_bigvgan_pretrain_32K.pth --wave test.wav

Data preprocessing

• 1， re-sampling: 32kHz

  python prepare/preprocess_a.py -w ./data_raw -o ./data_bigvgan/waves-32k

• 3， extract pitch

  python prepare/preprocess_f0.py -w data_bigvgan/waves-32k/ -p data_bigvgan/pitch

• 4， extract mel: [100, length]

  python prepare/preprocess_spec.py -w data_bigvgan/waves-32k/ -s data_bigvgan/mel

• 5， generate training index

  python prepare/preprocess_train.py

data_bigvgan/
│
└── 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
└── mel
     └── speaker0
     │      ├── 000001.mel.pt
     │      └── 000xxx.mel.pt
     └── speaker1
            ├── 000001.mel.pt
            └── 000xxx.mel.pt

Train

• 1， start training

  python nsf_bigvgan_trainer.py -c configs/nsf_bigvgan.yaml -n nsf_bigvgan

• 2， resume training

  python nsf_bigvgan_trainer.py -c configs/nsf_bigvgan.yaml -n nsf_bigvgan -p chkpt/nsf_bigvgan/***.pth

• 3， view log

  tensorboard --logdir logs/

Inference

• 1， export inference model

  python nsf_bigvgan_export.py --config configs/nsf_bigvgan.yaml --checkpoint_path chkpt/nsf_bigvgan/***.pt

• 2， extract mel

  python spec/inference.py -w test.wav -m test.mel.pt

• 3， extract F0

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

• 4， infer

  python nsf_bigvgan_inference.py --config configs/nsf_bigvgan.yaml --model nsf_bigvgan_g.pth --wave test.wav

  or

  python nsf_bigvgan_inference.py --config configs/nsf_bigvgan.yaml --model nsf_bigvgan_g.pth --mel test.mel.pt --pit test.csv

Augmentation of mel

For the over smooth output of acoustic model, we use gaussian blur for mel when train vocoder

# gaussian blur
model_b = get_gaussian_kernel(kernel_size=5, sigma=2, channels=1).to(device)
# mel blur
mel_b = mel[:, None, :, :]
mel_b = model_b(mel_b)
mel_b = torch.squeeze(mel_b, 1)
mel_r = torch.rand(1).to(device) * 0.5
mel_b = (1 - mel_r) * mel_b + mel_r * mel
# generator
optim_g.zero_grad()
fake_audio = model_g(mel_b, pit)

Source of code and References

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

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

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