mirror of https://github.com/coqui-ai/TTS.git
82 lines
2.7 KiB
Python
82 lines
2.7 KiB
Python
import unittest
|
|
import torch as T
|
|
|
|
from TTS.layers.tacotron import Prenet, CBHG, Decoder, Encoder
|
|
from TTS.layers.losses import L1LossMasked
|
|
from TTS.utils.generic_utils import sequence_mask
|
|
|
|
|
|
class PrenetTests(unittest.TestCase):
|
|
def test_in_out(self):
|
|
layer = Prenet(128, out_features=[256, 128])
|
|
dummy_input = T.rand(4, 128)
|
|
|
|
print(layer)
|
|
output = layer(dummy_input)
|
|
assert output.shape[0] == 4
|
|
assert output.shape[1] == 128
|
|
|
|
|
|
class CBHGTests(unittest.TestCase):
|
|
def test_in_out(self):
|
|
layer = CBHG(128, K=6, projections=[128, 128], num_highways=2)
|
|
dummy_input = T.rand(4, 8, 128)
|
|
|
|
print(layer)
|
|
output = layer(dummy_input)
|
|
assert output.shape[0] == 4
|
|
assert output.shape[1] == 8
|
|
assert output.shape[2] == 256
|
|
|
|
|
|
class DecoderTests(unittest.TestCase):
|
|
def test_in_out(self):
|
|
layer = Decoder(in_features=256, memory_dim=80, r=2)
|
|
dummy_input = T.rand(4, 8, 256)
|
|
dummy_memory = T.rand(4, 2, 80)
|
|
|
|
output, alignment, stop_tokens = layer(dummy_input, dummy_memory)
|
|
|
|
assert output.shape[0] == 4
|
|
assert output.shape[1] == 1, "size not {}".format(output.shape[1])
|
|
assert output.shape[2] == 80 * 2, "size not {}".format(output.shape[2])
|
|
assert stop_tokens.shape[0] == 4
|
|
assert stop_tokens.max() <= 1.0
|
|
assert stop_tokens.min() >= 0
|
|
|
|
|
|
class EncoderTests(unittest.TestCase):
|
|
def test_in_out(self):
|
|
layer = Encoder(128)
|
|
dummy_input = T.rand(4, 8, 128)
|
|
|
|
print(layer)
|
|
output = layer(dummy_input)
|
|
print(output.shape)
|
|
assert output.shape[0] == 4
|
|
assert output.shape[1] == 8
|
|
assert output.shape[2] == 256 # 128 * 2 BiRNN
|
|
|
|
|
|
class L1LossMaskedTests(unittest.TestCase):
|
|
def test_in_out(self):
|
|
layer = L1LossMasked()
|
|
dummy_input = T.ones(4, 8, 128).float()
|
|
dummy_target = T.ones(4, 8, 128).float()
|
|
dummy_length = (T.ones(4) * 8).long()
|
|
output = layer(dummy_input, dummy_target, dummy_length)
|
|
assert output.item() == 0.0
|
|
|
|
dummy_input = T.ones(4, 8, 128).float()
|
|
dummy_target = T.zeros(4, 8, 128).float()
|
|
dummy_length = (T.ones(4) * 8).long()
|
|
output = layer(dummy_input, dummy_target, dummy_length)
|
|
assert output.item() == 1.0, "1.0 vs {}".format(output.data[0])
|
|
dummy_input = T.ones(4, 8, 128).float()
|
|
dummy_target = T.zeros(4, 8, 128).float()
|
|
dummy_length = (T.arange(5, 9)).long()
|
|
mask = (
|
|
(sequence_mask(dummy_length).float() - 1.0) * 100.0).unsqueeze(2)
|
|
output = layer(dummy_input + mask, dummy_target, dummy_length)
|
|
assert output.item() == 1.0, "1.0 vs {}".format(output.data[0])
|