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import unittest
import torch
from training.loss import MultiResolutionSTFTLoss
class TestMultiResolutionSTFTLoss(unittest.TestCase):
def setUp(self):
torch.random.manual_seed(0)
self.loss_fn = MultiResolutionSTFTLoss([(1024, 120, 600), (2048, 240, 1200)])
self.x = torch.randn(
4,
16000,
)
self.y = torch.randn(
4,
16000,
)
def test_multi_resolution_stft_loss(self):
# Test the MultiResolutionSTFTLoss class with random input tensors
sc_loss, mag_loss = self.loss_fn(self.x, self.y)
self.assertIsInstance(sc_loss, torch.Tensor)
self.assertEqual(sc_loss.shape, torch.Size([]))
self.assertIsInstance(mag_loss, torch.Tensor)
self.assertEqual(mag_loss.shape, torch.Size([]))
def test_multi_resolution_stft_loss_nonzero(self):
# Test the MultiResolutionSTFTLoss class with input tensors that have a non-zero loss value
torch.manual_seed(0)
x = torch.randn(
4,
16000,
)
y = torch.randn(
4,
16000,
)
sc_loss, mag_loss = self.loss_fn(x, y)
self.assertIsInstance(sc_loss, torch.Tensor)
self.assertEqual(sc_loss.shape, torch.Size([]))
self.assertIsInstance(mag_loss, torch.Tensor)
self.assertEqual(mag_loss.shape, torch.Size([]))
expected_sc_loss = torch.tensor(
0.6571,
)
self.assertTrue(torch.allclose(sc_loss, expected_sc_loss, atol=1e-4))
expected_mag_loss = torch.tensor(
0.7007,
)
self.assertTrue(
torch.allclose(mag_loss, expected_mag_loss, rtol=1e-4, atol=1e-4),
)
if __name__ == "__main__":
unittest.main()
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