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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 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 | from __future__ import annotations
import json
import math
import subprocess
import sys
import tempfile
import unittest
from pathlib import Path
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT))
sys.path.insert(0, str(ROOT / "src"))
try:
import torch
except ImportError: # pragma: no cover
torch = None
from scripts import analyze_silence_sensitivity as analysis # noqa: E402
class AggregateSummaryTest(unittest.TestCase):
def test_paired_shift_and_flip_accounting(self) -> None:
labels = [0, 0, 1, 1]
baseline = [0.2, 0.8, 0.8, 0.2]
condition = [0.6, 0.4, 0.4, 0.7]
shift = analysis._probability_shift(labels, baseline, condition)
flips = analysis._decision_flips(labels, baseline, condition, threshold=0.5)
self.assertAlmostEqual(shift["mean"], 0.025)
self.assertAlmostEqual(shift["mean_absolute"], 0.425)
self.assertEqual(shift["increased_count"], 2)
self.assertEqual(shift["decreased_count"], 2)
self.assertEqual(flips["count"], 4)
self.assertEqual(flips["HOLD_to_END"], 2)
self.assertEqual(flips["END_to_HOLD"], 2)
self.assertEqual(flips["false_interruptions_introduced"], 1)
self.assertEqual(flips["false_interruptions_resolved"], 1)
self.assertEqual(flips["missed_ends_introduced"], 1)
self.assertEqual(flips["missed_ends_resolved"], 1)
@unittest.skipUnless(torch is not None, "PyTorch is not installed")
def test_append_then_suffix_crop_is_exact(self) -> None:
waveforms = [torch.tensor([1.0, 2.0, 3.0]), torch.tensor([4.0])]
padded, lengths = analysis._append_silence_and_pad(
waveforms,
silence_ms=200,
sample_rate=10,
max_seconds=0.4,
pad_side="left",
torch=torch,
)
self.assertEqual(lengths.tolist(), [4, 3])
self.assertEqual(
padded.tolist(),
[[2.0, 3.0, 0.0, 0.0], [0.0, 4.0, 0.0, 0.0]],
)
@unittest.skipUnless(torch is not None, "PyTorch is not installed")
class SilenceSensitivityCliTest(unittest.TestCase):
@staticmethod
def _write_checkpoint(path: Path) -> None:
from turn_detection.models.features import LogMelConfig
from turn_detection.models.tiny_tcn import TinyTCNConfig, TinyTurnDetector
torch.manual_seed(31)
model_config = TinyTCNConfig(
n_mels=8,
channels=4,
num_blocks=1,
kernel_size=2,
dilation_cycle=(1,),
attention_channels=3,
head_hidden=3,
dropout=0.0,
auxiliary_fillers=False,
)
feature_config = LogMelConfig(
sample_rate=8_000,
n_fft=64,
hop_length=32,
win_length=64,
n_mels=8,
f_max=4_000.0,
normalize=False,
center=False,
pad_side="left",
)
model = TinyTurnDetector(model_config)
torch.save(
{
"model_state": model.state_dict(),
"model_config": model.model_config(),
"threshold": 0.5,
"metadata": {
"feature_config": feature_config.__dict__,
"max_seconds": 0.25,
"data_scope": "unit-test aggregate",
"data_revision": "fixed-test-revision",
"run_metadata": {"status": "test"},
},
},
path,
)
@staticmethod
def _write_manifest(path: Path) -> None:
rows = []
for index in range(3):
samples = [
0.15 * math.sin(2.0 * math.pi * (180 + 20 * index) * sample / 8_000)
for sample in range(800 + index * 80)
]
rows.append(
{
"record_id": f"private-record-{index}",
"split": "validation",
"audio": samples,
"sample_rate": 8_000,
"endpoint": index % 2,
}
)
path.write_text(
"".join(json.dumps(row) + "\n" for row in rows),
encoding="utf-8",
)
def test_cli_is_deterministic_bounded_and_privacy_safe(self) -> None:
with tempfile.TemporaryDirectory() as directory:
root = Path(directory)
checkpoint = root / "checkpoint.pt"
source = root / "manifest.jsonl"
first_output = root / "first.json"
second_output = root / "second.json"
self._write_checkpoint(checkpoint)
self._write_manifest(source)
base_command = [
sys.executable,
str(ROOT / "scripts/analyze_silence_sensitivity.py"),
"--checkpoint",
str(checkpoint),
"--source",
str(source),
"--source-root",
str(root),
"--split",
"validation",
"--max-examples",
"2",
"--batch-size",
"2",
"--device",
"cpu",
]
for output in (first_output, second_output):
completed = subprocess.run(
[*base_command, "--output", str(output)],
cwd=ROOT,
check=False,
capture_output=True,
text=True,
)
self.assertEqual(completed.returncode, 0, completed.stderr)
first_text = first_output.read_text(encoding="utf-8")
second_text = second_output.read_text(encoding="utf-8")
report = json.loads(first_text)
self.assertEqual(first_text, second_text)
self.assertEqual(report["example_count"], 2)
self.assertEqual(report["positive_count"], 1)
self.assertEqual(report["negative_count"], 1)
self.assertEqual(report["selection"]["max_examples"], 2)
self.assertEqual(
[item["trailing_silence_ms"] for item in report["conditions"]],
[0, 200, 400, 800],
)
for condition in report["conditions"]:
self.assertEqual(condition["classification_metrics"]["count"], 2)
self.assertEqual(condition["probability_summary"]["count"], 2)
self.assertIsNone(report["conditions"][0]["relative_to_0ms"])
for condition in report["conditions"][1:]:
relative = condition["relative_to_0ms"]
self.assertEqual(relative["probability_shift"]["count"], 2)
self.assertEqual(
relative["threshold_decision_flips"]["count"]
+ relative["threshold_decision_flips"]["unchanged_count"],
2,
)
self.assertTrue(report["privacy"]["aggregate_only"])
self.assertNotIn("private-record", first_text)
self.assertNotIn("record_id", first_text)
if __name__ == "__main__":
unittest.main()
|