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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 | |