def __init__(self, avg=.9998):
super(Model, self).__init__()
# Getting Mel Spectrogram on the fly
self.mel_layer = Spectrogram.MelSpectrogram(sr=fs,
n_fft=n_fft,
n_mels=n_mels,
htk=htk,
fmin=50,
fmax=6000,
center=center)
# Creating Layers
self.linear = torch.nn.Linear(n_mels * regions, m, bias=False)
torch.nn.init.constant_(self.linear.weight, 0) # initialize
self.avg = avg
def __init__(self, avg=.9998):
super(Model, self).__init__()
# Getting Mel Spectrogram on the fly
self.spec_layer = Spectrogram.MelSpectrogram(sr=fs, n_fft=n_fft, n_mels=n_mels, htk=htk, fmin=50, fmax=6000, center=center)
# Creating Layers
self.CNN_freq_kernel_size=(128,1)
self.CNN_freq_kernel_stride=(2,1)
k_out = 128
k2_out = 256
self.CNN_freq = nn.Conv2d(1,k_out,
kernel_size=self.CNN_freq_kernel_size,stride=self.CNN_freq_kernel_stride)
self.CNN_time = nn.Conv2d(k_out,k2_out,
kernel_size=(1,regions),stride=(1,1))
self.region_v = 1 + (n_mels-self.CNN_freq_kernel_size[0])//self.CNN_freq_kernel_stride[0]
self.linear = torch.nn.Linear(k2_out*self.region_v, m, bias=False)
self.avg = avg