mirror of https://github.com/coqui-ai/TTS.git
87 lines
2.9 KiB
Python
87 lines
2.9 KiB
Python
import unittest
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import torch as T
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from TTS.layers.tacotron import Prenet, CBHG, Decoder, Encoder
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from layers.losses import L1LossMasked, _sequence_mask
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class PrenetTests(unittest.TestCase):
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def test_in_out(self):
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layer = Prenet(128, out_features=[256, 128])
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dummy_input = T.autograd.Variable(T.rand(4, 128))
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print(layer)
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output = layer(dummy_input)
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assert output.shape[0] == 4
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assert output.shape[1] == 128
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class CBHGTests(unittest.TestCase):
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def test_in_out(self):
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layer = CBHG(128, K= 6, projections=[128, 128], num_highways=2)
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dummy_input = T.autograd.Variable(T.rand(4, 8, 128))
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print(layer)
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output = layer(dummy_input)
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assert output.shape[0] == 4
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assert output.shape[1] == 8
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assert output.shape[2] == 256
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class DecoderTests(unittest.TestCase):
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def test_in_out(self):
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layer = Decoder(in_features=128, memory_dim=32, r=5)
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dummy_input = T.autograd.Variable(T.rand(4, 8, 128))
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dummy_memory = T.autograd.Variable(T.rand(4, 120, 32))
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print(layer)
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output, alignment = layer(dummy_input, dummy_memory)
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print(output.shape)
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assert output.shape[0] == 4
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assert output.shape[1] == 120 / 5
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assert output.shape[2] == 32 * 5
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class EncoderTests(unittest.TestCase):
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def test_in_out(self):
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layer = Encoder(128)
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dummy_input = T.autograd.Variable(T.rand(4, 8, 128))
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print(layer)
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output = layer(dummy_input)
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print(output.shape)
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assert output.shape[0] == 4
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assert output.shape[1] == 8
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assert output.shape[2] == 256 # 128 * 2 BiRNN
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class L1LossMaskedTests(unittest.TestCase):
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def test_in_out(self):
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layer = L1LossMasked()
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dummy_input = T.autograd.Variable(T.ones(4, 8, 128).float())
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dummy_target = T.autograd.Variable(T.ones(4, 8, 128).float())
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dummy_length = T.autograd.Variable((T.ones(4) * 8).long())
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output = layer(dummy_input, dummy_target, dummy_length)
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assert output.shape[0] == 1
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assert len(output.shape) == 1
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assert output.data[0] == 0.0
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dummy_input = T.autograd.Variable(T.ones(4, 8, 128).float())
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dummy_target = T.autograd.Variable(T.zeros(4, 8, 128).float())
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dummy_length = T.autograd.Variable((T.ones(4) * 8).long())
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output = layer(dummy_input, dummy_target, dummy_length)
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assert output.data[0] == 1.0, "1.0 vs {}".format(output.data[0])
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dummy_input = T.autograd.Variable(T.ones(4, 8, 128).float())
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dummy_target = T.autograd.Variable(T.zeros(4, 8, 128).float())
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dummy_length = T.autograd.Variable((T.arange(5,9)).long())
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mask = ((_sequence_mask(dummy_length).float() - 1.0) * 100.0).unsqueeze(2)
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output = layer(dummy_input + mask, dummy_target, dummy_length)
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assert output.data[0] == 1.0, "1.0 vs {}".format(output.data[0])
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