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import unittest
import torch as T
from tests import get_tests_input_path
from TTS.encoder.losses import AngleProtoLoss, GE2ELoss, SoftmaxAngleProtoLoss
from TTS.encoder.models.lstm import LSTMSpeakerEncoder
from TTS.encoder.models.resnet import ResNetSpeakerEncoder
file_path = get_tests_input_path()
class LSTMSpeakerEncoderTests(unittest.TestCase):
# pylint: disable=R0201
def test_in_out(self):
dummy_input = T.rand(4, 80, 20) # B x D x T
dummy_hidden = [T.rand(2, 4, 128), T.rand(2, 4, 128)]
model = LSTMSpeakerEncoder(input_dim=80, proj_dim=256, lstm_dim=768, num_lstm_layers=3)
# computing d vectors
output = model.forward(dummy_input)
assert output.shape[0] == 4
assert output.shape[1] == 256
output = model.inference(dummy_input)
assert output.shape[0] == 4
assert output.shape[1] == 256
# compute d vectors by passing LSTM hidden
# output = model.forward(dummy_input, dummy_hidden)
# assert output.shape[0] == 4
# assert output.shape[1] == 20
# assert output.shape[2] == 256
# check normalization
output_norm = T.nn.functional.normalize(output, dim=1, p=2)
assert_diff = (output_norm - output).sum().item()
assert output.type() == "torch.FloatTensor"
assert abs(assert_diff) < 1e-4, f" [!] output_norm has wrong values - {assert_diff}"
# compute d for a given batch
dummy_input = T.rand(1, 80, 240) # B x T x D
output = model.compute_embedding(dummy_input, num_frames=160, num_eval=5)
assert output.shape[0] == 1
assert output.shape[1] == 256
assert len(output.shape) == 2
class ResNetSpeakerEncoderTests(unittest.TestCase):
# pylint: disable=R0201
def test_in_out(self):
dummy_input = T.rand(4, 80, 20) # B x D x T
dummy_hidden = [T.rand(2, 4, 128), T.rand(2, 4, 128)]
model = ResNetSpeakerEncoder(input_dim=80, proj_dim=256)
# computing d vectors
output = model.forward(dummy_input)
assert output.shape[0] == 4
assert output.shape[1] == 256
output = model.forward(dummy_input, l2_norm=True)
assert output.shape[0] == 4
assert output.shape[1] == 256
# check normalization
output_norm = T.nn.functional.normalize(output, dim=1, p=2)
assert_diff = (output_norm - output).sum().item()
assert output.type() == "torch.FloatTensor"
assert abs(assert_diff) < 1e-4, f" [!] output_norm has wrong values - {assert_diff}"
# compute d for a given batch
dummy_input = T.rand(1, 80, 240) # B x D x T
output = model.compute_embedding(dummy_input, num_frames=160, num_eval=10)
assert output.shape[0] == 1
assert output.shape[1] == 256
assert len(output.shape) == 2
class GE2ELossTests(unittest.TestCase):
# pylint: disable=R0201
def test_in_out(self):
# check random input
dummy_input = T.rand(4, 5, 64) # num_speaker x num_utterance x dim
loss = GE2ELoss(loss_method="softmax")
output = loss.forward(dummy_input)
assert output.item() >= 0.0
# check all zeros
dummy_input = T.ones(4, 5, 64) # num_speaker x num_utterance x dim
loss = GE2ELoss(loss_method="softmax")
output = loss.forward(dummy_input)
assert output.item() >= 0.0
# check speaker loss with orthogonal d-vectors
dummy_input = T.empty(3, 64)
dummy_input = T.nn.init.orthogonal_(dummy_input)
dummy_input = T.cat(
[
dummy_input[0].repeat(5, 1, 1).transpose(0, 1),
dummy_input[1].repeat(5, 1, 1).transpose(0, 1),
dummy_input[2].repeat(5, 1, 1).transpose(0, 1),
]
) # num_speaker x num_utterance x dim
loss = GE2ELoss(loss_method="softmax")
output = loss.forward(dummy_input)
assert output.item() < 0.005
class AngleProtoLossTests(unittest.TestCase):
# pylint: disable=R0201
def test_in_out(self):
# check random input
dummy_input = T.rand(4, 5, 64) # num_speaker x num_utterance x dim
loss = AngleProtoLoss()
output = loss.forward(dummy_input)
assert output.item() >= 0.0
# check all zeros
dummy_input = T.ones(4, 5, 64) # num_speaker x num_utterance x dim
loss = AngleProtoLoss()
output = loss.forward(dummy_input)
assert output.item() >= 0.0
# check speaker loss with orthogonal d-vectors
dummy_input = T.empty(3, 64)
dummy_input = T.nn.init.orthogonal_(dummy_input)
dummy_input = T.cat(
[
dummy_input[0].repeat(5, 1, 1).transpose(0, 1),
dummy_input[1].repeat(5, 1, 1).transpose(0, 1),
dummy_input[2].repeat(5, 1, 1).transpose(0, 1),
]
) # num_speaker x num_utterance x dim
loss = AngleProtoLoss()
output = loss.forward(dummy_input)
assert output.item() < 0.005
class SoftmaxAngleProtoLossTests(unittest.TestCase):
# pylint: disable=R0201
def test_in_out(self):
embedding_dim = 64
num_speakers = 5
batch_size = 4
dummy_label = T.randint(low=0, high=num_speakers, size=(batch_size, num_speakers))
# check random input
dummy_input = T.rand(batch_size, num_speakers, embedding_dim) # num_speaker x num_utterance x dim
loss = SoftmaxAngleProtoLoss(embedding_dim=embedding_dim, n_speakers=num_speakers)
output = loss.forward(dummy_input, dummy_label)
assert output.item() >= 0.0
# check all zeros
dummy_input = T.ones(batch_size, num_speakers, embedding_dim) # num_speaker x num_utterance x dim
loss = SoftmaxAngleProtoLoss(embedding_dim=embedding_dim, n_speakers=num_speakers)
output = loss.forward(dummy_input, dummy_label)
assert output.item() >= 0.0
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