def test_rnn_separator_forward_backward_complex(input_dim, rnn_type, layer,
unit, dropout, num_spk,
nonlinear):
model = RNNSeparator(
input_dim=input_dim,
rnn_type=rnn_type,
layer=layer,
unit=unit,
dropout=dropout,
num_spk=num_spk,
nonlinear=nonlinear,
)
model.train()
real = torch.rand(2, 10, input_dim)
imag = torch.rand(2, 10, input_dim)
x = ComplexTensor(real, imag)
x_lens = torch.tensor([10, 8], dtype=torch.long)
masked, flens, others = model(x, ilens=x_lens)
assert isinstance(masked[0], ComplexTensor)
assert len(masked) == num_spk
masked[0].abs().mean().backward()
def test_rnn_separator_invalid_type():
with pytest.raises(ValueError):
RNNSeparator(
input_dim=10,
rnn_type="rnn",
layer=2,
unit=10,
dropout=0.1,
num_spk=2,
nonlinear="fff",
)
def test_rnn_separator_output():
x = torch.rand(2, 10, 10)
x_lens = torch.tensor([10, 8], dtype=torch.long)
for num_spk in range(1, 3):
model = RNNSeparator(
input_dim=10,
rnn_type="rnn",
layer=2,
unit=10,
dropout=0.1,
num_spk=num_spk,
nonlinear="relu",
)
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
from espnet2.enh.separator.rnn_separator import RNNSeparator
from espnet2.layers.label_aggregation import LabelAggregate
enh_stft_encoder = STFTEncoder(
n_fft=32,
hop_length=16,
)
enh_stft_decoder = STFTDecoder(
n_fft=32,
hop_length=16,
)
enh_rnn_separator = RNNSeparator(
input_dim=17,
layer=1,
unit=10,
num_spk=1,
)
si_snr_loss = SISNRLoss()
fix_order_solver = FixedOrderSolver(criterion=si_snr_loss)
default_frontend = DefaultFrontend(
fs=300,
n_fft=32,
win_length=32,
hop_length=24,
n_mels=32,
)
conv_encoder = ConvEncoder(
channel=15,
kernel_size=32,
stride=16,
)
conv_decoder = ConvDecoder(
channel=15,
kernel_size=32,
stride=16,
)
rnn_separator = RNNSeparator(
input_dim=15,
layer=1,
unit=10,
)
dprnn_separator = DPRNNSeparator(input_dim=15, layer=1, unit=10, segment_size=4)
tcn_separator = TCNSeparator(
input_dim=15,
layer=2,
stack=1,
bottleneck_dim=10,
hidden_dim=10,
kernel=3,
)
transformer_separator = TransformerSeparator(
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)
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,