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<div align="center">
<h1> Neural Source-Filter BigVGAN </h1>
    Just For Fun
</div>

![nsf_bigvgan_mel](https://github.com/PlayVoice/NSF-BigVGAN/assets/16432329/eebb8dca-a8d3-4e69-b02c-632a3a1cdd6a)

## Dataset preparation

Put the dataset into the data_raw directory according to the following file structure
```shell
data_raw
β”œβ”€β”€β”€speaker0
β”‚   β”œβ”€β”€β”€000001.wav
β”‚   β”œβ”€β”€β”€...
β”‚   └───000xxx.wav
└───speaker1
    β”œβ”€β”€β”€000001.wav
    β”œβ”€β”€β”€...
    └───000xxx.wav
```

## Install dependencies

- 1 software dependency
  
  > pip install -r requirements.txt

- 2 download [release](https://github.com/PlayVoice/NSF-BigVGAN/releases/tag/debug) model, and test
  
  > python nsf_bigvgan_inference.py --config configs/nsf_bigvgan.yaml --model nsf_bigvgan_g.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

```shell
data_bigvgan/
β”‚
└── waves-32k
β”‚    └── speaker0
β”‚    β”‚      β”œβ”€β”€ 000001.wav
β”‚    β”‚      └── 000xxx.wav
β”‚    └── speaker1
β”‚           β”œβ”€β”€ 000001.wav
β”‚           └── 000xxx.wav
└── pitch
β”‚    └── speaker0
β”‚    β”‚      β”œβ”€β”€ 000001.pit.npy
β”‚    β”‚      └── 000xxx.pit.npy
β”‚    └── speaker1
β”‚           β”œβ”€β”€ 000001.pit.npy
β”‚           └── 000xxx.pit.npy
└── mel
     └── speaker0
     β”‚      β”œβ”€β”€ 000001.mel.pt
     β”‚      └── 000xxx.mel.pt
     └── speaker1
            β”œβ”€β”€ 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/maxgan.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)
```
![mel_gaussian_blur](https://github.com/PlayVoice/NSF-BigVGAN/assets/16432329/7fa96ef7-5e3b-4ae6-bc61-9b6da3b9d0b9)

## 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://arxiv.org/abs/2106.07889)

https://github.com/NVIDIA/BigVGAN [[paper]](https://arxiv.org/abs/2206.04658)