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35d483e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | 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()
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