File size: 8,817 Bytes
4b0b144 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 | """Tests for ShiftySpeech Audio Deepfake Detection API.
Tests cover:
- Health endpoint
- Predict endpoint with real audio
- Predict endpoint with fake audio
- Audio preprocessing (pad/trim/tile)
- Error handling for invalid input
- Response schema validation
"""
import base64
import io
import os
import sys
import warnings
import numpy as np
import pytest
warnings.filterwarnings("ignore", category=DeprecationWarning)
# Monkey-patch omegaconf before importing api module
import omegaconf._utils as _omegaconf_utils
if not hasattr(_omegaconf_utils, "is_primitive_type"):
_omegaconf_utils.is_primitive_type = lambda t: t in (int, float, bool, str, bytes)
# Add model code path for local testing
SERVICE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
MODEL_CODE_PATH = os.path.join(
SERVICE_DIR, "synthetic_speech_detection", "SSL_Anti-spoofing"
)
if MODEL_CODE_PATH not in sys.path:
sys.path.insert(0, MODEL_CODE_PATH)
# Patch the api module constants for local testing
import api
api.MODEL_CODE_PATH = MODEL_CODE_PATH
api.WEIGHTS_PATH = os.path.join(SERVICE_DIR, "weights", "hfg_aug_1_2.pt")
api.XLSR_DIR = os.path.join(SERVICE_DIR, "models")
from fastapi.testclient import TestClient
client = TestClient(api.app)
# Dataset paths
DATASET_DIR = os.path.join(
os.path.dirname(SERVICE_DIR),
os.pardir,
os.pardir,
"dataset",
"audio",
)
DATASET_DIR = os.path.normpath(DATASET_DIR)
REAL_DIR = os.path.join(DATASET_DIR, "real")
FAKE_DIR = os.path.join(DATASET_DIR, "fake")
# Check if model weights are available for integration tests
WEIGHTS_AVAILABLE = os.path.exists(api.WEIGHTS_PATH) and os.path.exists(
os.path.join(SERVICE_DIR, "models", "xlsr2_300m.pt")
)
DATASET_AVAILABLE = os.path.isdir(REAL_DIR) and os.path.isdir(FAKE_DIR)
def _make_wav_bytes(duration_s: float = 1.0, sr: int = 16000) -> bytes:
"""Generate a simple sine wave WAV file as bytes."""
import soundfile as sf
t = np.linspace(0, duration_s, int(sr * duration_s), endpoint=False)
audio = 0.5 * np.sin(2 * np.pi * 440 * t).astype(np.float32)
buf = io.BytesIO()
sf.write(buf, audio, sr, format="WAV")
buf.seek(0)
return buf.read()
def _encode_file(path: str) -> str:
"""Read a file and return base64 encoded string."""
with open(path, "rb") as f:
return base64.b64encode(f.read()).decode("utf-8")
class TestHealthEndpoint:
"""Tests for the /health endpoint."""
def test_health_returns_200(self):
response = client.get("/health")
assert response.status_code == 200
def test_health_contains_model_name(self):
response = client.get("/health")
data = response.json()
assert data["model"] == "shiftyspeech"
def test_health_contains_device(self):
response = client.get("/health")
data = response.json()
assert data["device"] == "cpu"
def test_health_contains_status(self):
response = client.get("/health")
data = response.json()
assert data["status"] in ("healthy", "degraded")
class TestPreprocessAudio:
"""Tests for audio preprocessing logic."""
def test_preprocess_short_audio_tiles(self):
"""Short audio should be tiled to TARGET_SAMPLES."""
wav_bytes = _make_wav_bytes(duration_s=0.5, sr=16000)
tensor = api.preprocess_audio(wav_bytes)
assert tensor.shape == (1, api.TARGET_SAMPLES)
def test_preprocess_long_audio_trims(self):
"""Long audio should be trimmed to TARGET_SAMPLES."""
wav_bytes = _make_wav_bytes(duration_s=10.0, sr=16000)
tensor = api.preprocess_audio(wav_bytes)
assert tensor.shape == (1, api.TARGET_SAMPLES)
def test_preprocess_exact_length(self):
"""Audio at exact TARGET_SAMPLES should pass through."""
duration = api.TARGET_SAMPLES / api.SAMPLE_RATE
wav_bytes = _make_wav_bytes(duration_s=duration, sr=16000)
tensor = api.preprocess_audio(wav_bytes)
assert tensor.shape == (1, api.TARGET_SAMPLES)
def test_preprocess_resamples_from_8khz(self):
"""Audio at 8kHz should be resampled to 16kHz."""
wav_bytes = _make_wav_bytes(duration_s=1.0, sr=8000)
tensor = api.preprocess_audio(wav_bytes)
assert tensor.shape == (1, api.TARGET_SAMPLES)
def test_preprocess_invalid_input_raises(self):
"""Invalid audio bytes should raise ValueError."""
with pytest.raises(ValueError):
api.preprocess_audio(b"not audio data")
@pytest.mark.skipif(
not WEIGHTS_AVAILABLE,
reason="Model weights not available locally",
)
class TestPredictEndpoint:
"""Integration tests for the /predict endpoint (requires weights)."""
def test_predict_returns_200(self):
wav_bytes = _make_wav_bytes(duration_s=2.0)
b64 = base64.b64encode(wav_bytes).decode("utf-8")
response = client.post("/predict", json={"audio_data": b64})
assert response.status_code == 200
def test_predict_response_schema(self):
wav_bytes = _make_wav_bytes(duration_s=2.0)
b64 = base64.b64encode(wav_bytes).decode("utf-8")
response = client.post("/predict", json={"audio_data": b64})
data = response.json()
assert "model" in data
assert "probability" in data
assert "prediction" in data
assert "class" in data
assert "inference_time" in data
assert data["model"] == "shiftyspeech"
def test_predict_probability_in_range(self):
wav_bytes = _make_wav_bytes(duration_s=2.0)
b64 = base64.b64encode(wav_bytes).decode("utf-8")
response = client.post("/predict", json={"audio_data": b64})
data = response.json()
assert 0.0 <= data["probability"] <= 1.0
def test_predict_class_matches_prediction(self):
wav_bytes = _make_wav_bytes(duration_s=2.0)
b64 = base64.b64encode(wav_bytes).decode("utf-8")
response = client.post("/predict", json={"audio_data": b64})
data = response.json()
if data["prediction"] == 1:
assert data["class"] == "fake"
else:
assert data["class"] == "real"
def test_predict_custom_threshold(self):
wav_bytes = _make_wav_bytes(duration_s=2.0)
b64 = base64.b64encode(wav_bytes).decode("utf-8")
response = client.post(
"/predict",
json={"audio_data": b64, "threshold": 0.99},
)
data = response.json()
assert response.status_code == 200
# With threshold=0.99, only very high prob_fake => fake
if data["probability"] < 0.99:
assert data["prediction"] == 0
assert data["class"] == "real"
def test_predict_inference_time_positive(self):
wav_bytes = _make_wav_bytes(duration_s=2.0)
b64 = base64.b64encode(wav_bytes).decode("utf-8")
response = client.post("/predict", json={"audio_data": b64})
data = response.json()
assert data["inference_time"] > 0
@pytest.mark.skipif(
not WEIGHTS_AVAILABLE or not DATASET_AVAILABLE,
reason="Model weights or dataset not available",
)
class TestRealDataset:
"""Integration tests using actual dataset files."""
def test_predict_real_audio(self):
"""Test prediction on a real audio file."""
path = os.path.join(REAL_DIR, "real_0.wav")
if not os.path.exists(path):
pytest.skip("real_0.wav not found")
b64 = _encode_file(path)
response = client.post("/predict", json={"audio_data": b64})
assert response.status_code == 200
data = response.json()
assert 0.0 <= data["probability"] <= 1.0
def test_predict_fake_audio(self):
"""Test prediction on a fake audio file."""
path = os.path.join(FAKE_DIR, "fake_1.wav")
if not os.path.exists(path):
pytest.skip("fake_1.wav not found")
b64 = _encode_file(path)
response = client.post("/predict", json={"audio_data": b64})
assert response.status_code == 200
data = response.json()
assert 0.0 <= data["probability"] <= 1.0
class TestPredictValidation:
"""Tests for input validation on /predict."""
def test_predict_missing_audio_data(self):
response = client.post("/predict", json={})
assert response.status_code == 422
def test_predict_invalid_base64(self):
response = client.post("/predict", json={"audio_data": "not-valid-base64!!!"})
# Should return 500 (decode error) or 422
assert response.status_code in (400, 422, 500)
def test_predict_threshold_out_of_range(self):
response = client.post(
"/predict",
json={"audio_data": "dGVzdA==", "threshold": 1.5},
)
assert response.status_code == 422
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