Awesome
<div align="center"> <h1> Neural Source-Filter BigVGAN </h1> Just For Fun </div>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
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1 software dependency
pip install -r requirements.txt
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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
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1， re-sampling: 32kHz
python prepare/preprocess_a.py -w ./data_raw -o ./data_bigvgan/waves-32k
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3， extract pitch
python prepare/preprocess_f0.py -w data_bigvgan/waves-32k/ -p data_bigvgan/pitch
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4， extract mel: [100, length]
python prepare/preprocess_spec.py -w data_bigvgan/waves-32k/ -s data_bigvgan/mel
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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
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1， start training
python nsf_bigvgan_trainer.py -c configs/nsf_bigvgan.yaml -n nsf_bigvgan
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2， resume training
python nsf_bigvgan_trainer.py -c configs/nsf_bigvgan.yaml -n nsf_bigvgan -p chkpt/nsf_bigvgan/***.pth
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3， view log
tensorboard --logdir logs/
Inference
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1， export inference model
python nsf_bigvgan_export.py --config configs/nsf_bigvgan.yaml --checkpoint_path chkpt/nsf_bigvgan/***.pt
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2， extract mel
python spec/inference.py -w test.wav -m test.mel.pt
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3， extract F0
python pitch/inference.py -w test.wav -p test.csv
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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