File size: 6,225 Bytes
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 | from __future__ import annotations
import sys
import unittest
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src"))
try:
import torch
except ImportError: # pragma: no cover - lightweight CI
torch = None
@unittest.skipUnless(torch is not None, "PyTorch is not installed")
class TinyTCNTest(unittest.TestCase):
def setUp(self) -> None:
from turn_detection.models.tiny_tcn import TinyTCNConfig, TinyTurnDetector
self.config = TinyTCNConfig(
channels=32,
num_blocks=3,
kernel_size=3,
dilation_cycle=(1, 2, 4),
attention_channels=16,
head_hidden=16,
dropout=0.0,
)
torch.manual_seed(3)
self.model = TinyTurnDetector(self.config).eval()
def test_output_shapes_are_logits(self) -> None:
features = torch.randn(3, 80, 51)
mask = torch.ones(3, 51, dtype=torch.bool)
output = self.model(features, mask)
self.assertEqual(tuple(output.endpoint_logits.shape), (3,))
self.assertEqual(tuple(output.midfiller_logits.shape), (3,))
self.assertEqual(tuple(output.endfiller_logits.shape), (3,))
self.assertTrue(bool(torch.isfinite(output.endpoint_logits).all()))
def test_right_padding_does_not_change_valid_prediction(self) -> None:
features = torch.randn(2, 80, 40)
mask = torch.ones(2, 40, dtype=torch.bool)
padded = torch.cat([features, torch.randn(2, 80, 17) * 100], dim=-1)
padded_mask = torch.cat([mask, torch.zeros(2, 17, dtype=torch.bool)], dim=-1)
with torch.inference_mode():
original = self.model(features, mask).endpoint_logits
after_padding = self.model(padded, padded_mask).endpoint_logits
torch.testing.assert_close(original, after_padding, atol=1e-6, rtol=1e-6)
def test_default_model_is_in_intended_tiny_parameter_range(self) -> None:
from turn_detection.models.tiny_tcn import TinyTurnDetector
count = sum(parameter.numel() for parameter in TinyTurnDetector().parameters())
self.assertGreaterEqual(count, 300_000)
self.assertLess(count, 1_000_000)
def test_invalid_feature_shape_is_rejected(self) -> None:
with self.assertRaises(ValueError):
self.model(torch.randn(2, 79, 20))
@unittest.skipUnless(torch is not None, "PyTorch is not installed")
class FrontendTest(unittest.TestCase):
def test_feature_and_mask_lengths(self) -> None:
from turn_detection.models.features import LogMelFrontend
frontend = LogMelFrontend()
waveform = torch.randn(2, 16_000)
features, mask = frontend(waveform, torch.tensor([16_000, 8_000]))
self.assertEqual(tuple(features.shape[:2]), (2, 80))
self.assertEqual(int(mask[0].sum()), 98)
self.assertEqual(int(mask[1].sum()), 48)
self.assertTrue(bool((features[1, :, ~mask[1]] == 0).all()))
def test_deployment_frontend_parity(self) -> None:
try:
import numpy as np
except ImportError:
self.skipTest("numpy is not installed")
from turn_detection.models.features import LogMelConfig, LogMelFrontend
from turn_detection.runtime.features import FrontendConfig, log_mel_spectrogram
audio = np.random.default_rng(17).standard_normal(12_345).astype(np.float32) * 0.1
runtime_features, runtime_mask = log_mel_spectrogram(
audio,
16_000,
FrontendConfig(max_seconds=1.0, normalization="whisper", pad_side="left"),
)
training_frontend = LogMelFrontend(
LogMelConfig(
normalize=False,
mel_scale="htk",
log_scale="whisper",
center=True,
drop_last_frame=True,
pad_side="left",
)
)
padded = torch.zeros(1, 16_000)
padded[0, -len(audio) :] = torch.from_numpy(audio)
training_features, training_mask = training_frontend(padded, torch.tensor([len(audio)]))
torch.testing.assert_close(
training_features[0],
torch.from_numpy(runtime_features),
atol=5e-6,
rtol=1e-5,
)
self.assertEqual(training_mask[0].to(torch.float32).tolist(), runtime_mask.tolist())
def test_export_metadata_loads_in_runtime(self) -> None:
import json
import tempfile
from turn_detection.models.deployment import build_runtime_metadata
from turn_detection.models.features import LogMelConfig
from turn_detection.runtime.predictor import ModelMetadata
config = LogMelConfig(
normalize=False,
log_scale="whisper",
center=True,
drop_last_frame=True,
pad_side="left",
)
payload = build_runtime_metadata(
config,
max_seconds=4.0,
threshold=0.61,
model_name="preview",
architecture="tiny_tcn",
development_only=True,
training_status="preview-only",
data_scope="one shard",
data_revision="abc123",
parameter_count=151_812,
)
with tempfile.TemporaryDirectory() as directory:
path = Path(directory) / "model_metadata.json"
path.write_text(json.dumps(payload), encoding="utf-8")
loaded = ModelMetadata.from_path(path)
self.assertEqual(loaded.input_features_name, "log_mel")
self.assertEqual(loaded.frame_mask_name, "frame_mask")
self.assertEqual(loaded.output_type, "probability")
self.assertEqual(loaded.frontend.target_frames, 400)
self.assertTrue(loaded.development_only)
self.assertEqual(loaded.training_status, "preview-only")
self.assertEqual(loaded.data_scope, "one shard")
self.assertEqual(loaded.data_revision, "abc123")
self.assertEqual(loaded.parameter_count, 151_812)
self.assertEqual(loaded.controller.endpoint_threshold, 0.61)
self.assertAlmostEqual(loaded.controller.long_pause_threshold, 0.43)
if __name__ == "__main__":
unittest.main()
|