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import unittest
import torch
from models.config import AcousticENModelConfig
from training.loss.delightful_tts_loss import (
DelightfulTTSLoss,
ForwardSumLoss,
sample_wise_min_max,
sequence_mask,
)
class TestLosses(unittest.TestCase):
def setUp(self):
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def test_sequence_mask(self):
sequence_length = torch.tensor([2, 3, 4], device=self.device)
max_len = 5
mask = sequence_mask(sequence_length, max_len)
expected_mask = torch.tensor([[True, True, False, False, False],
[True, True, True, False, False],
[True, True, True, True, False]], device=self.device)
self.assertTrue(torch.equal(mask, expected_mask))
def test_sample_wise_min_max(self):
x = torch.tensor([[[1, 1], [1, 0], [0, 1]]], dtype=torch.float32, device=self.device)
normalized_x = sample_wise_min_max(x)
expected_normalized_x = torch.tensor([[
[1., 1.],
[1., 0.],
[0., 1.],
]], device=self.device)
self.assertTrue(torch.allclose(normalized_x, expected_normalized_x))
def test_ForwardSumLoss(self):
loss_function = ForwardSumLoss()
attn_logprob = torch.randn((1, 1, 11, 11))
src_lens = torch.ones((1,), dtype=torch.long)
mel_lens = torch.ones((1,), dtype=torch.long)
loss = loss_function(attn_logprob, src_lens, mel_lens)
self.assertTrue(isinstance(loss, torch.Tensor))
def test_DelightfulTTSLoss(self):
model_config = AcousticENModelConfig()
loss_function = DelightfulTTSLoss(model_config)
mel_output = torch.randn((1, 11, 11))
mel_target = torch.randn((1, 11, 11))
mel_lens = torch.ones((1,), dtype=torch.long)
dur_output = torch.randn((1, 11))
dur_target = torch.randn((1, 11))
pitch_output = torch.randn((1, 11))
pitch_target = torch.randn((1, 11))
energy_output = torch.randn((1, 11))
energy_target = torch.randn((1, 11))
src_lens = torch.ones((1,), dtype=torch.long)
p_prosody_ref = torch.randn((1, 11, 11))
p_prosody_pred = torch.randn((1, 11, 11))
u_prosody_ref = torch.randn((1, 11, 11))
u_prosody_pred = torch.randn((1, 11, 11))
aligner_logprob = torch.randn((1, 1, 11, 11))
aligner_hard = torch.randn((1, 11, 11))
aligner_soft = torch.randn((1, 11, 11))
total_loss, _, _, _, _, _, _, _, _, _ = loss_function(
mel_output, mel_target, mel_lens, dur_output, dur_target, pitch_output, pitch_target,
energy_output, energy_target, src_lens, p_prosody_ref, p_prosody_pred,
u_prosody_ref, u_prosody_pred, aligner_logprob, aligner_hard, aligner_soft,
)
self.assertTrue(isinstance(total_loss, torch.Tensor))
if __name__ == "__main__":
unittest.main()
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