def test_mfcc(self, batch_size, num_features, num_classes, input_length):
model = Wav2Letter(input_type="mfcc", num_features=13)
x = torch.rand(batch_size, num_features, input_length)
out = model(x)
assert out.size() == (batch_size, num_classes, 2)
def test_waveform(self, batch_size, num_features, num_classes,
input_length):
model = Wav2Letter()
x = torch.rand(batch_size, num_features, input_length)
out = model(x)
assert out.size() == (batch_size, num_classes, 2)
def test_waveform(self):
batch_size = 2
num_features = 1
num_classes = 40
input_length = 320
model = Wav2Letter(num_classes=num_classes, num_features=num_features)
x = torch.rand(batch_size, num_features, input_length)
out = model(x)
assert out.size() == (batch_size, num_classes, 2)
def test_mfcc(self):
batch_size = 2
num_features = 13
num_classes = 40
input_length = 2
model = Wav2Letter(num_classes=num_classes, input_type="mfcc", num_features=num_features)
x = torch.rand(batch_size, num_features, input_length)
out = model(x)
assert out.size() == (batch_size, num_classes, 2)