class WaveNet(chainer.Chain):
def __init__(self, n_loop, n_layer, a_channels, r_channels, s_channels,
use_embed_tanh):
super(WaveNet, self).__init__()
with self.init_scope():
self.embed = L.Convolution2D(a_channels,
r_channels, (2, 1),
pad=(1, 0),
nobias=True)
self.resnet = ResidualNet(n_loop, n_layer, 2, r_channels,
2 * r_channels, s_channels)
self.proj1 = L.Convolution2D(s_channels,
s_channels,
1,
nobias=True)
self.proj2 = L.Convolution2D(s_channels,
a_channels,
1,
nobias=True)
self.a_channels = a_channels
self.s_channels = s_channels
self.use_embed_tanh = use_embed_tanh
def __call__(self, x, condition, generating=False):
length = x.shape[2]
x = self.embed(x)
x = x[:, :, :length, :] # crop
if self.use_embed_tanh:
x = F.tanh(x)
z = F.relu(self.resnet(x, condition))
z = F.relu(self.proj1(z))
y = self.proj2(z)
return y
def initialize(self, n):
self.resnet.initialize(n)
self.embed.pad = (0, 0)
self.embed_queue = chainer.Variable(
self.xp.zeros((n, self.a_channels, 2, 1), dtype=self.xp.float32))
self.proj1_queue = chainer.Variable(
self.xp.zeros((n, self.s_channels, 1, 1), dtype=self.xp.float32))
self.proj2_queue3 = chainer.Variable(
self.xp.zeros((n, self.s_channels, 1, 1), dtype=self.xp.float32))
def generate(self, x, condition):
self.embed_queue = F.concat((self.embed_queue[:, :, 1:], x), axis=2)
x = self.embed(self.embed_queue)
if self.use_embed_tanh:
x = F.tanh(x)
x = F.relu(self.resnet.generate(x, condition))
self.proj1_queue = F.concat((self.proj1_queue[:, :, 1:], x), axis=2)
x = F.relu(self.proj1(self.proj1_queue))
self.proj2_queue3 = F.concat((self.proj2_queue3[:, :, 1:], x), axis=2)
x = self.proj2(self.proj2_queue3)
return x
class WaveNet(nn.Module):
def __init__(self,
n_loop=4,
n_layer=10,
a_channels=256,
r_channels=64,
s_channels=256,
use_embed_tanh=True):
super().__init__()
self.embed = nn.Conv1d(a_channels,
r_channels,
2,
padding=1,
bias=False)
self.resnet = ResidualNet(n_loop, n_layer, 2, r_channels,
2 * r_channels, s_channels)
self.proj1 = nn.Conv1d(s_channels, s_channels, 1, bias=False)
self.proj2 = nn.Conv1d(s_channels, a_channels, 1, bias=False)
self.a_channels = a_channels
self.s_channels = s_channels
self.use_embed_tanh = use_embed_tanh
def forward(self, x, condition):
length = x.size(2)
x = self.embed(x)
x = x[:, :, :length] # crop
if self.use_embed_tanh:
x = torch.tanh(x)
z = F.relu(self.resnet(x, condition))
z = F.relu(self.proj1(z))
y = self.proj2(z)
return y
def initialize(self, n):
self.resnet.initialize(n)
self.embed.padding = 0
self.embed_queue = torch.zeros((n, self.a_channels, 2),
dtype=self.embed.weight.dtype)
self.proj1_queue = torch.zeros((n, self.s_channels, 1),
dtype=self.proj1.weight.dtype)
self.proj2_queue = torch.zeros((n, self.s_channels, 1),
dtype=self.proj2.weight.dtype)
def generate(self, x, condition):
self.embed_queue = torch.cat((self.embed_queue[:, :, 1:], x), dim=2)
x = self.embed(self.embed_queue)
if self.use_embed_tanh:
x = torch.tanh(x)
x = F.relu(self.resnet.generate(x, condition))
self.proj1_queue = torch.cat((self.proj1_queue[:, :, 1:], x), dim=2)
x = F.relu(self.proj1(self.proj1_queue))
self.proj2_queue = torch.cat((self.proj2_queue[:, :, 1:], x), dim=2)
x = self.proj2(self.proj2_queue)
return x
class WaveNet(chainer.Chain):
def __init__(self, n_loop, n_layer, a_channels, r_channels, s_channels,
use_embed_tanh):
super(WaveNet, self).__init__()
with self.init_scope():
self.embed = L.Convolution2D(
a_channels, r_channels, (2, 1), pad=(1, 0), nobias=True)
self.resnet = ResidualNet(
n_loop, n_layer, 2, r_channels, 2 * r_channels, s_channels)
self.proj1 = L.Convolution2D(
s_channels, s_channels, 1, nobias=True)
self.proj2 = L.Convolution2D(
s_channels, a_channels, 1, nobias=True)
self.a_channels = a_channels
self.s_channels = s_channels
self.use_embed_tanh = use_embed_tanh
def __call__(self, x, condition, generating=False):
length = x.shape[2]
x = self.embed(x)
x = x[:, :, :length, :] # crop
if self.use_embed_tanh:
x = F.tanh(x)
z = F.relu(self.resnet(x, condition))
z = F.relu(self.proj1(z))
y = self.proj2(z)
return y
def initialize(self, n):
self.resnet.initialize(n)
self.embed.pad = (0, 0)
self.embed_queue = chainer.Variable(self.xp.zeros(
(n, self.a_channels, 2, 1), dtype=self.embed.W.dtype))
self.proj1_queue = chainer.Variable(self.xp.zeros(
(n, self.s_channels, 1, 1), dtype=self.proj1.W.dtype))
self.proj2_queue3 = chainer.Variable(self.xp.zeros(
(n, self.s_channels, 1, 1), dtype=self.proj2.W.dtype))
def generate(self, x, condition):
self.embed_queue = F.concat((self.embed_queue[:, :, 1:], x), axis=2)
x = self.embed(self.embed_queue)
if self.use_embed_tanh:
x = F.tanh(x)
x = F.relu(self.resnet.generate(x, condition))
self.proj1_queue = F.concat((self.proj1_queue[:, :, 1:], x), axis=2)
x = F.relu(self.proj1(self.proj1_queue))
self.proj2_queue3 = F.concat((self.proj2_queue3[:, :, 1:], x), axis=2)
x = self.proj2(self.proj2_queue3)
return x
def __init__(self,
n_loop=4,
n_layer=10,
a_channels=256,
r_channels=64,
s_channels=256,
use_embed_tanh=True):
super().__init__()
self.embed = nn.Conv1d(a_channels,
r_channels,
2,
padding=1,
bias=False)
self.resnet = ResidualNet(n_loop, n_layer, 2, r_channels,
2 * r_channels, s_channels)
self.proj1 = nn.Conv1d(s_channels, s_channels, 1, bias=False)
self.proj2 = nn.Conv1d(s_channels, a_channels, 1, bias=False)
self.a_channels = a_channels
self.s_channels = s_channels
self.use_embed_tanh = use_embed_tanh
def __init__(self, n_loop, n_layer, a_channels, r_channels, s_channels,
use_embed_tanh):
super(WaveNet, self).__init__()
with self.init_scope():
self.embed = L.Convolution2D(a_channels,
r_channels, (2, 1),
pad=(1, 0),
nobias=True)
self.resnet = ResidualNet(n_loop, n_layer, 2, r_channels,
2 * r_channels, s_channels)
self.proj1 = L.Convolution2D(s_channels,
s_channels,
1,
nobias=True)
self.proj2 = L.Convolution2D(s_channels,
a_channels,
1,
nobias=True)
self.a_channels = a_channels
self.s_channels = s_channels
self.use_embed_tanh = use_embed_tanh
def __init__(self, n_loop, n_layer, a_channels, r_channels, s_channels,
use_embed_tanh):
super(WaveNet, self).__init__()
with self.init_scope():
self.embed = L.Convolution2D(
a_channels, r_channels, (2, 1), pad=(1, 0), nobias=True)
self.resnet = ResidualNet(
n_loop, n_layer, 2, r_channels, 2 * r_channels, s_channels)
self.proj1 = L.Convolution2D(
s_channels, s_channels, 1, nobias=True)
self.proj2 = L.Convolution2D(
s_channels, a_channels, 1, nobias=True)
self.a_channels = a_channels
self.s_channels = s_channels
self.use_embed_tanh = use_embed_tanh