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| import unittest | |
| import torch | |
| from training.loss.metrics import Metrics | |
| class TestMetrics(unittest.TestCase): | |
| def setUp(self): | |
| self.metrics = Metrics() | |
| # Set the frequency of the pitch (in Hz) | |
| self.pitch_freq = 440.0 | |
| self.duration = 1.0 | |
| self.sr = 22050 | |
| # Generate a time vector for the audio signal | |
| self.t = torch.linspace(0, self.duration, int(self.sr * self.duration)) | |
| # Generate a sinusoidal waveform with the specified pitch frequency | |
| self.audio = torch.sin(2 * torch.pi * self.pitch_freq * self.t).unsqueeze(0) | |
| def test_calculate_mcd(self): | |
| wav_targets = torch.randn(1, 22050) | |
| wav_predictions = torch.randn(1, 22050) | |
| mcd = self.metrics.calculate_mcd(wav_targets, wav_predictions) | |
| self.assertIsInstance(mcd, torch.Tensor) | |
| def test_calculate_spectrogram_distance(self): | |
| wav_targets = torch.randn(1, 22050) | |
| wav_predictions = torch.randn(1, 22050) | |
| dist = self.metrics.calculate_spectrogram_distance(wav_targets, wav_predictions) | |
| self.assertIsInstance(dist, torch.Tensor) | |
| def test_calculate_f0_rmse(self): | |
| wav_targets = torch.randn(1, 22050) | |
| wav_predictions = torch.randn(1, 22050) | |
| rmse = self.metrics.calculate_f0_rmse(wav_targets, wav_predictions) | |
| self.assertIsInstance(rmse, float) | |
| def test_calculate_jitter_shimmer(self): | |
| jitter, shimmer = self.metrics.calculate_jitter_shimmer(self.audio) | |
| self.assertIsInstance(jitter, float) | |
| self.assertIsInstance(shimmer, float) | |
| def test_wav_metrics(self): | |
| ermr, jitter, shimmer = self.metrics.wav_metrics(self.audio) | |
| self.assertIsInstance(ermr, float) | |
| self.assertIsInstance(jitter, float) | |
| self.assertIsInstance(shimmer, float) | |
| if __name__ == "__main__": | |
| unittest.main() | |