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from torch import nn
from .constants import * # noqa: F403
from .deepunet import DeepUnet, DeepUnet0
from .seq import BiGRU
from .spec import MelSpectrogram
class E2E(nn.Module):
def __init__(self, hop_length, n_blocks, n_gru, kernel_size, en_de_layers=5, inter_layers=4, in_channels=1,
en_out_channels=16):
super(E2E, self).__init__()
self.mel = MelSpectrogram(N_MELS, SAMPLE_RATE, WINDOW_LENGTH, hop_length, None, MEL_FMIN, MEL_FMAX) # noqa: F405
self.unet = DeepUnet(kernel_size, n_blocks, en_de_layers, inter_layers, in_channels, en_out_channels)
self.cnn = nn.Conv2d(en_out_channels, 3, (3, 3), padding=(1, 1))
if n_gru:
self.fc = nn.Sequential(
BiGRU(3 * N_MELS, 256, n_gru), # noqa: F405
nn.Linear(512, N_CLASS), # noqa: F405
nn.Dropout(0.25),
nn.Sigmoid()
)
else:
self.fc = nn.Sequential(
nn.Linear(3 * N_MELS, N_CLASS), # noqa: F405
nn.Dropout(0.25),
nn.Sigmoid()
)
def forward(self, x):
mel = self.mel(x.reshape(-1, x.shape[-1])).transpose(-1, -2).unsqueeze(1)
x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2)
# x = self.fc(x)
hidden_vec = 0
if len(self.fc) == 4:
for i in range(len(self.fc)):
x = self.fc[i](x)
if i == 0:
hidden_vec = x
return hidden_vec, x
class E2E0(nn.Module):
def __init__(self, n_blocks, n_gru, kernel_size, en_de_layers=5, inter_layers=4, in_channels=1,
en_out_channels=16):
super(E2E0, self).__init__()
self.unet = DeepUnet0(kernel_size, n_blocks, en_de_layers, inter_layers, in_channels, en_out_channels)
self.cnn = nn.Conv2d(en_out_channels, 3, (3, 3), padding=(1, 1))
if n_gru:
self.fc = nn.Sequential(
BiGRU(3 * N_MELS, 256, n_gru), # noqa: F405
nn.Linear(512, N_CLASS), # noqa: F405
nn.Dropout(0.25),
nn.Sigmoid()
)
else:
self.fc = nn.Sequential(
nn.Linear(3 * N_MELS, N_CLASS), # noqa: F405
nn.Dropout(0.25),
nn.Sigmoid()
)
def forward(self, mel):
mel = mel.transpose(-1, -2).unsqueeze(1)
x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2)
x = self.fc(x)
return x
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