def test_convtasnet_sep():
nnet = ConvTasNet(
n_src=2,
n_repeats=2,
n_blocks=3,
bn_chan=16,
hid_chan=4,
skip_chan=8,
n_filters=32,
)
# Test torch input
wav = torch.rand(1, 800)
out = nnet.separate(wav)
assert isinstance(out, torch.Tensor)
# Test numpy input
wav = np.random.randn(1, 800).astype("float32")
out = nnet.separate(wav)
assert isinstance(out, np.ndarray)
# Test str input
sf.write("tmp.wav", wav[0], 8000)
nnet.separate("tmp.wav")
# Warning when overwriting
with pytest.warns(UserWarning):
nnet.separate("tmp.wav")
# Test with bad samplerate
sf.write("tmp.wav", wav[0], 16000)
# Raises
with pytest.raises(RuntimeError):
nnet.separate("tmp.wav", force_overwrite=True)
# Resamples
nnet.separate("tmp.wav", force_overwrite=True, resample=True)
def test_convtasnet_sep():
nnet = ConvTasNet(
n_src=2, n_repeats=2, n_blocks=3, bn_chan=16, hid_chan=4, skip_chan=8, n_filters=32
)
# Test torch input
wav = torch.rand(1, 800)
out = nnet.separate(wav)
assert isinstance(out, torch.Tensor)
# Test numpy input
wav = np.random.randn(1, 800).astype("float32")
out = nnet.separate(wav)
assert isinstance(out, np.ndarray)
# Test str input
sf.write("tmp.wav", wav[0], 8000)
nnet.separate("tmp.wav")
def test_save_and_load_convtasnet(fb):
model1 = ConvTasNet(n_src=2,
n_repeats=2,
n_blocks=2,
bn_chan=16,
hid_chan=4,
skip_chan=8,
n_filters=32,
fb_name=fb)
test_input = torch.randn(1, 800)
model_conf = model1.serialize()
reconstructed_model = ConvTasNet.from_pretrained(model_conf)
assert_allclose(model1.separate(test_input),
reconstructed_model(test_input))
