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64 lines
1.9 KiB
64 lines
1.9 KiB
2 years ago
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import torch
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import torchaudio
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class MelSpec(torch.nn.Module):
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def __init__(self,
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sample_rate=8000,
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stft_n=1024,
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stft_hop=256,
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f_min=300,
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f_max=4000,
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n_mels=256,
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naf_mode=False,
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mel_log='log',
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spec_norm='l2'):
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super(MelSpec, self).__init__()
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self.naf_mode = naf_mode
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self.mel_log = mel_log
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self.spec_norm = spec_norm
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self.mel = torchaudio.transforms.MelSpectrogram(
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sample_rate=sample_rate,
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n_fft=stft_n,
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hop_length=stft_hop,
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f_min=f_min,
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f_max=f_max,
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n_mels=n_mels,
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window_fn=torch.hann_window,
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power = 1 if naf_mode else 2,
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pad_mode = 'constant' if naf_mode else 'reflect',
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norm = 'slaney' if naf_mode else None,
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mel_scale = 'slaney' if naf_mode else 'htk'
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)
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def forward(self, x):
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# normalize volume
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p = 1e999 if self.spec_norm == 'max' else 2
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x = torch.nn.functional.normalize(x, p=p, dim=-1)
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if self.naf_mode:
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x = self.mel(x) + 0.06
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else:
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x = self.mel(x) + 1e-8
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if self.mel_log == 'log10':
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x = torch.log10(x)
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elif self.mel_log == 'log':
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x = torch.log(x)
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if self.spec_norm == 'max':
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x = x - torch.amax(x, dim=(-2,-1), keepdim=True)
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return x
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def build_mel_spec_layer(params):
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return MelSpec(
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sample_rate = params['sample_rate'],
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stft_n = params['stft_n'],
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stft_hop = params['stft_hop'],
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f_min = params['f_min'],
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f_max = params['f_max'],
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n_mels = params['n_mels'],
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naf_mode = params.get('naf_mode', False),
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mel_log = params.get('mel_log', 'log'),
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spec_norm = params.get('spec_norm', 'l2')
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)
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