def __init__(self):
super(AudioEncoder, self).__init__()
seq = SequentialMaker()
seq.add_module(
"conv_0",
MaskedConv1d(Hyper.dim_f, Hyper.dim_d, 1, padding="causal"))
seq.add_module("relu_0", nn.ReLU())
seq.add_module("drop_0", nn.Dropout(Hyper.dropout))
seq.add_module(
"conv_1",
MaskedConv1d(Hyper.dim_d, Hyper.dim_d, 1, padding="causal"))
seq.add_module("relu_1", nn.ReLU())
seq.add_module("drop_1", nn.Dropout(Hyper.dropout))
seq.add_module(
"relu_2",
MaskedConv1d(Hyper.dim_d, Hyper.dim_d, 1, padding="causal"))
seq.add_module("drop_2", nn.Dropout(Hyper.dropout))
i = 3
for _ in range(2):
for j in range(4):
seq.add_module(
"highway-conv_{}".format(i),
HighwayConv1d(Hyper.dim_d,
kernel_size=3,
dilation=3**j,
padding="causal"))
seq.add_module("drop_{}".format(i), nn.Dropout(Hyper.dropout))
i += 1
for k in range(2):
seq.add_module(
"highway-conv_{}".format(i),
HighwayConv1d(Hyper.dim_d,
kernel_size=3,
dilation=3,
padding="causal"))
if k == 0:
seq.add_module("drop_{}".format(i), nn.Dropout(Hyper.dropout))
i += 1
self.seq_ = seq()
def print_shape(self, input_shape):
print("text-encode {")
SequentialMaker.print_shape(self.seq_,
torch.LongTensor(np.zeros(input_shape)),
intent_size=2)
print("}")
def print_shape(self, input_shape):
print("super-resolution {")
SequentialMaker.print_shape(self.seq_,
torch.FloatTensor(np.zeros(input_shape)),
intent_size=2)
print("}")
def __init__(self):
super(SuperRes, self).__init__()
seq = SequentialMaker()
seq.add_module(
"conv_0", MaskedConv1d(Hyper.dim_f, Hyper.dim_c, 1,
padding="same"))
seq.add_module("drop_0", nn.Dropout(Hyper.dropout))
i = 1
for _ in range(1):
for j in range(2):
seq.add_module(
"highway-conv_{}".format(i),
HighwayConv1d(Hyper.dim_c,
kernel_size=3,
dilation=3**j,
padding="same"))
seq.add_module("drop_{}".format(i), nn.Dropout(Hyper.dropout))
i += 1
for _ in range(2):
seq.add_module(
"deconv_{}".format(i),
Deconv1d(Hyper.dim_c, Hyper.dim_c, 2, padding="same"))
seq.add_module("drop_{}".format(i), nn.Dropout(Hyper.dropout))
i += 1
for j in range(2):
seq.add_module(
"highway-conv_{}".format(i),
HighwayConv1d(Hyper.dim_c,
kernel_size=3,
dilation=3**j,
padding="same"))
seq.add_module("drop_{}".format(i), nn.Dropout(Hyper.dropout))
i += 1
seq.add_module(
"conv_{}".format(i),
MaskedConv1d(Hyper.dim_c, Hyper.dim_c * 2, 1, padding="same"))
seq.add_module("drop_{}".format(i), nn.Dropout(Hyper.dropout))
i += 1
for _ in range(2):
seq.add_module(
"highway-conv_{}".format(i),
HighwayConv1d(Hyper.dim_c * 2,
kernel_size=3,
dilation=1,
padding="same"))
seq.add_module("drop_{}".format(i), nn.Dropout(Hyper.dropout))
i += 1
F = Hyper.audio_nfft // 2 + 1
seq.add_module("conv_{}".format(i),
MaskedConv1d(Hyper.dim_c * 2, F, 1, padding="same"))
seq.add_module("drop_{}".format(i), nn.Dropout(Hyper.dropout))
i += 1
for _ in range(2):
seq.add_module("conv_{}".format(i),
MaskedConv1d(F, F, 1, padding="same"))
seq.add_module("relu_{}".format(i), nn.ReLU())
seq.add_module("drop_{}".format(i), nn.Dropout(Hyper.dropout))
i += 1
seq.add_module("conv_{}".format(i),
MaskedConv1d(F, F, 1, padding="same"))
self.seq_ = seq()
self.sigmoid_ = nn.Sigmoid()
def print_shape(self, input_shape):
print("audio-decoder {")
SequentialMaker.print_shape(self.seq_,
torch.FloatTensor(np.zeros(input_shape)),
intent_size=2)
print("}")
def __init__(self):
super(TextEncoder, self).__init__()
seq = SequentialMaker()
seq.add_module(
"char-embed",
CharEmbed(len(Hyper.vocab), Hyper.dim_e, Hyper.vocab.find('P')))
seq.add_module(
"conv_0",
MaskedConv1d(Hyper.dim_e, Hyper.dim_d * 2, 1, padding="same"))
seq.add_module("relu_0", nn.ReLU())
seq.add_module("drop_0", nn.Dropout(Hyper.dropout))
seq.add_module(
"conv_1",
MaskedConv1d(Hyper.dim_d * 2, Hyper.dim_d * 2, 1, padding="same"))
seq.add_module("drop_1", nn.Dropout(Hyper.dropout))
i = 2
for _ in range(2):
for j in range(4):
seq.add_module(
"highway-conv_{}".format(i),
HighwayConv1d(Hyper.dim_d * 2,
kernel_size=3,
dilation=3**j,
padding="same"))
seq.add_module("drop_{}".format(i), nn.Dropout(Hyper.dropout))
i += 1
for j in [1, 0]:
for k in range(2):
seq.add_module(
"highway-conv_{}".format(i),
HighwayConv1d(Hyper.dim_d * 2,
kernel_size=3**j,
dilation=1,
padding="same"))
if not (j == 0 and k == 1):
seq.add_module("drop_{}".format(i),
nn.Dropout(Hyper.dropout))
i += 1
self.seq_ = seq()