| from __future__ import annotations |
|
|
| import importlib.util |
| import json |
| import tempfile |
| import unittest |
| from pathlib import Path |
|
|
| from turn_detection.runtime.features import FrontendConfig |
| from turn_detection.runtime.predictor import ModelMetadata |
|
|
|
|
| class FrontendConfigTest(unittest.TestCase): |
| def test_frame_contract(self) -> None: |
| config = FrontendConfig() |
| self.assertEqual(config.max_samples, 128_000) |
| self.assertEqual(config.target_frames, 800) |
|
|
| def test_invalid_bounds_are_rejected(self) -> None: |
| with self.assertRaises(ValueError): |
| FrontendConfig(f_max=20_000) |
|
|
| def test_metadata_round_trip(self) -> None: |
| metadata = ModelMetadata(model_name="smoke", architecture="tinytcn") |
| with tempfile.TemporaryDirectory() as directory: |
| path = Path(directory) / "model_metadata.json" |
| path.write_text(json.dumps(metadata.to_dict()), encoding="utf-8") |
| loaded = ModelMetadata.from_path(path) |
| self.assertEqual(loaded, metadata) |
|
|
| @unittest.skipUnless( |
| importlib.util.find_spec("numpy"), "numpy is optional in this test environment" |
| ) |
| def test_features_have_expected_shape_and_finite_values(self) -> None: |
| import numpy as np |
|
|
| from turn_detection.runtime.features import log_mel_spectrogram |
|
|
| audio = np.zeros(16_000, dtype=np.float32) |
| features, mask = log_mel_spectrogram(audio, 16_000) |
| self.assertEqual(features.shape, (80, 800)) |
| self.assertEqual(mask.shape, (800,)) |
| self.assertTrue(np.isfinite(features).all()) |
| self.assertEqual(int(mask.sum()), 100) |
|
|
|
|
| if __name__ == "__main__": |
| unittest.main() |
|
|
