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import numpy as np | |
import torch | |
from TTS.vocoder.models.parallel_wavegan_discriminator import ( | |
ParallelWaveganDiscriminator, | |
ResidualParallelWaveganDiscriminator, | |
) | |
def test_pwgan_disciminator(): | |
model = ParallelWaveganDiscriminator( | |
in_channels=1, | |
out_channels=1, | |
kernel_size=3, | |
num_layers=10, | |
conv_channels=64, | |
dilation_factor=1, | |
nonlinear_activation="LeakyReLU", | |
nonlinear_activation_params={"negative_slope": 0.2}, | |
bias=True, | |
) | |
dummy_x = torch.rand((4, 1, 64 * 256)) | |
output = model(dummy_x) | |
assert np.all(output.shape == (4, 1, 64 * 256)) | |
model.remove_weight_norm() | |
def test_redisual_pwgan_disciminator(): | |
model = ResidualParallelWaveganDiscriminator( | |
in_channels=1, | |
out_channels=1, | |
kernel_size=3, | |
num_layers=30, | |
stacks=3, | |
res_channels=64, | |
gate_channels=128, | |
skip_channels=64, | |
dropout=0.0, | |
bias=True, | |
nonlinear_activation="LeakyReLU", | |
nonlinear_activation_params={"negative_slope": 0.2}, | |
) | |
dummy_x = torch.rand((4, 1, 64 * 256)) | |
output = model(dummy_x) | |
assert np.all(output.shape == (4, 1, 64 * 256)) | |
model.remove_weight_norm() | |