def test_in_out(self):
layer = CBHG(128, K= 6, projections=[128, 128], num_highways=2)
dummy_input = T.autograd.Variable(T.rand(4, 8, 128))
print(layer)
output = layer(dummy_input)
assert output.shape[0] == 4
assert output.shape[1] == 8
assert output.shape[2] == 256
def __init__(self, embedding_dim=256, linear_dim=1025, mel_dim=80,
r=5, padding_idx=None):
super(Tacotron, self).__init__()
self.r = r
self.mel_dim = mel_dim
self.linear_dim = linear_dim
self.embedding = nn.Embedding(len(symbols), embedding_dim,
padding_idx=padding_idx)
print(" | > Embedding dim : {}".format(len(symbols)))
self.embedding.weight.data.normal_(0, 0.3)
self.encoder = Encoder(embedding_dim)
self.decoder = Decoder(256, mel_dim, r)
self.postnet = CBHG(mel_dim, K=8, projections=[256, mel_dim])
self.last_linear = nn.Linear(mel_dim * 2, linear_dim)
def test_in_out(self):
layer = self.cbhg = CBHG(128,
K=8,
conv_bank_features=80,
conv_projections=[160, 128],
highway_features=80,
gru_features=80,
num_highways=4)
dummy_input = T.rand(4, 8, 128)
print(layer)
output = layer(dummy_input)
assert output.shape[0] == 4
assert output.shape[1] == 8
assert output.shape[2] == 160
def test_in_out(self):
#pylint: disable=attribute-defined-outside-init
layer = self.cbhg = CBHG(128,
K=8,
conv_bank_features=80,
conv_projections=[160, 128],
highway_features=80,
gru_features=80,
num_highways=4)
# B x D x T
dummy_input = T.rand(4, 128, 8)
print(layer)
output = layer(dummy_input)
assert output.shape[0] == 4
assert output.shape[1] == 8
assert output.shape[2] == 160