def test_dc_crn_separator_invalid_type():
with pytest.raises(ValueError):
DC_CRNSeparator(
input_dim=17,
input_channels=[2, 2, 4],
mode="xxx",
)
def test_dc_crn_separator_invalid_enc_layer():
with pytest.raises(AssertionError):
DC_CRNSeparator(
input_dim=17,
input_channels=[2, 2, 4],
enc_layers=1,
)
def test_dc_crn_separator_forward_backward_complex(
input_dim,
num_spk,
input_channels,
enc_hid_channels,
enc_layers,
glstm_groups,
glstm_layers,
glstm_bidirectional,
glstm_rearrange,
mode,
):
model = DC_CRNSeparator(
input_dim=input_dim,
num_spk=num_spk,
input_channels=input_channels,
enc_hid_channels=enc_hid_channels,
enc_kernel_size=(1, 3),
enc_padding=(0, 1),
enc_last_kernel_size=(1, 3),
enc_last_stride=(1, 2),
enc_last_padding=(0, 1),
enc_layers=enc_layers,
skip_last_kernel_size=(1, 3),
skip_last_stride=(1, 1),
skip_last_padding=(0, 1),
glstm_groups=glstm_groups,
glstm_layers=glstm_layers,
glstm_bidirectional=glstm_bidirectional,
glstm_rearrange=glstm_rearrange,
mode=mode,
)
model.train()
real = torch.rand(2, 10, input_dim)
imag = torch.rand(2, 10, input_dim)
x = torch.complex(real, imag) if is_torch_1_9_plus else ComplexTensor(
real, imag)
x_lens = torch.tensor([10, 8], dtype=torch.long)
masked, flens, others = model(x, ilens=x_lens)
assert is_complex(masked[0])
assert len(masked) == num_spk
masked[0].abs().mean().backward()
def test_dc_crn_separator_output():
real = torch.rand(2, 10, 17)
imag = torch.rand(2, 10, 17)
x = torch.complex(real, imag) if is_torch_1_9_plus else ComplexTensor(
real, imag)
x_lens = torch.tensor([10, 8], dtype=torch.long)
for num_spk in range(1, 3):
model = DC_CRNSeparator(
input_dim=17,
num_spk=num_spk,
input_channels=[2, 2, 4],
)
model.eval()
specs, _, others = model(x, x_lens)
assert isinstance(specs, list)
assert isinstance(others, dict)
for n in range(num_spk):
assert "mask_spk{}".format(n + 1) in others
assert specs[n].shape == others["mask_spk{}".format(n + 1)].shape
def test_dc_crn_separator_multich_input(
num_spk,
input_channels,
enc_kernel_size,
enc_padding,
enc_last_kernel_size,
enc_last_stride,
enc_last_padding,
skip_last_kernel_size,
skip_last_stride,
skip_last_padding,
):
model = DC_CRNSeparator(
input_dim=33,
num_spk=num_spk,
input_channels=input_channels,
enc_hid_channels=2,
enc_kernel_size=enc_kernel_size,
enc_padding=enc_padding,
enc_last_kernel_size=enc_last_kernel_size,
enc_last_stride=enc_last_stride,
enc_last_padding=enc_last_padding,
enc_layers=3,
skip_last_kernel_size=skip_last_kernel_size,
skip_last_stride=skip_last_stride,
skip_last_padding=skip_last_padding,
)
model.train()
real = torch.rand(2, 10, input_channels[0] // 2, 33)
imag = torch.rand(2, 10, input_channels[0] // 2, 33)
x = torch.complex(real, imag) if is_torch_1_9_plus else ComplexTensor(
real, imag)
x_lens = torch.tensor([10, 8], dtype=torch.long)
masked, flens, others = model(x, ilens=x_lens)
assert is_complex(masked[0])
assert len(masked) == num_spk
masked[0].abs().mean().backward()
stft_encoder_bultin_complex = STFTEncoder(n_fft=32,
hop_length=16,
use_builtin_complex=True)
stft_decoder = STFTDecoder(n_fft=32, hop_length=16)
conv_encoder = ConvEncoder(channel=17, kernel_size=36, stride=18)
conv_decoder = ConvDecoder(channel=17, kernel_size=36, stride=18)
null_encoder = NullEncoder()
null_decoder = NullDecoder()
dc_crn_separator = DC_CRNSeparator(input_dim=17, input_channels=[2, 2, 4])
dccrn_separator = DCCRNSeparator(input_dim=17,
num_spk=1,
kernel_num=[32, 64, 128])
dprnn_separator = DPRNNSeparator(input_dim=17,
layer=1,
unit=10,
segment_size=4)
dptnet_separator = DPTNetSeparator(input_dim=16,
layer=1,
unit=10,
segment_size=4)
stft_decoder = STFTDecoder(n_fft=32, hop_length=16)
conv_encoder = ConvEncoder(channel=17, kernel_size=36, stride=18)
conv_decoder = ConvDecoder(channel=17, kernel_size=36, stride=18)
null_encoder = NullEncoder()
null_decoder = NullDecoder()
conformer_separator = ConformerSeparator(
input_dim=17, predict_noise=True, adim=8, aheads=2, layers=2, linear_units=10
)
dc_crn_separator = DC_CRNSeparator(
input_dim=17, predict_noise=True, input_channels=[2, 2, 4]
)
dccrn_separator = DCCRNSeparator(
input_dim=17, num_spk=1, kernel_num=[32, 64, 128], use_noise_mask=True
)
dprnn_separator = DPRNNSeparator(
input_dim=17, predict_noise=True, layer=1, unit=10, segment_size=4
)
dptnet_separator = DPTNetSeparator(
input_dim=16, predict_noise=True, layer=1, unit=10, segment_size=4
)
rnn_separator = RNNSeparator(input_dim=17, predict_noise=True, layer=1, unit=10)