def test_svoice_separator_forward_backward(
input_dim,
enc_dim,
kernel_size,
hidden_size,
num_spk,
num_layers,
segment_size,
bidirectional,
input_normalize,
):
model = SVoiceSeparator(
input_dim=input_dim,
enc_dim=enc_dim,
kernel_size=kernel_size,
hidden_size=hidden_size,
num_spk=num_spk,
num_layers=num_layers,
segment_size=segment_size,
bidirectional=bidirectional,
input_normalize=input_normalize,
)
model.train()
x = torch.rand(2, 800)
x_lens = torch.tensor([400, 300], dtype=torch.long)
separated, _, _ = model(x, ilens=x_lens)
assert isinstance(separated[0][0], Tensor)
assert len(separated) == num_layers
separated[0][0].mean().backward()
def test_svoice_separator_output_eval():
x = torch.rand(2, 800)
x_lens = torch.tensor([10, 8], dtype=torch.long)
for num_spk in range(1, 3):
model = SVoiceSeparator(
input_dim=12,
enc_dim=8,
kernel_size=8,
hidden_size=8,
num_spk=num_spk,
num_layers=4,
segment_size=2,
bidirectional=False,
input_normalize=False,
)
model.eval()
waveforms, _, _ = model(x, x_lens)
assert isinstance(waveforms, list)
assert x[0].shape == waveforms[0][0].shape
unit=10,
segment_size=4)
dptnet_separator = DPTNetSeparator(input_dim=16,
layer=1,
unit=10,
segment_size=4)
rnn_separator = RNNSeparator(input_dim=17, layer=1, unit=10)
svoice_separator = SVoiceSeparator(
input_dim=17,
enc_dim=4,
kernel_size=4,
hidden_size=4,
num_spk=2,
num_layers=2,
segment_size=4,
bidirectional=False,
input_normalize=False,
)
tcn_separator = TCNSeparator(
input_dim=17,
layer=2,
stack=1,
bottleneck_dim=10,
hidden_dim=10,
kernel=3,
)