File size: 6,687 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 | """Evaluate ShiftySpeech SSL-AASIST on the DeepSafe audio dataset.
Reports accuracy, precision, recall, F1, EER, and per-file results.
"""
import os
import sys
import time
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
# Monkey-patch omegaconf for fairseq compatibility
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)
import argparse
import librosa
import numpy as np
import torch
# Add model code to path
SERVICE_DIR = os.path.dirname(os.path.abspath(__file__))
MODEL_CODE_PATH = os.path.join(
SERVICE_DIR, "synthetic_speech_detection", "SSL_Anti-spoofing"
)
sys.path.insert(0, MODEL_CODE_PATH)
from model import Model as SSLAASISTModel
SAMPLE_RATE = 16000
TARGET_SAMPLES = 64600
DATASET_DIR = os.path.join(
SERVICE_DIR, os.pardir, os.pardir, os.pardir, "dataset", "audio"
)
DATASET_DIR = os.path.normpath(DATASET_DIR)
def pad_audio(audio, target=TARGET_SAMPLES):
"""Pad/trim audio to target length using tiling."""
if len(audio) >= target:
return audio[:target]
num_repeats = target // len(audio) + 1
return np.tile(audio, num_repeats)[:target]
def compute_eer(target_scores, nontarget_scores):
"""Compute Equal Error Rate."""
n_scores = target_scores.size + nontarget_scores.size
all_scores = np.concatenate((target_scores, nontarget_scores))
labels = np.concatenate(
(np.ones(target_scores.size), np.zeros(nontarget_scores.size))
)
indices = np.argsort(all_scores, kind="mergesort")
labels = labels[indices]
tar_trial_sums = np.cumsum(labels)
nontarget_trial_sums = nontarget_scores.size - (
np.arange(1, n_scores + 1) - tar_trial_sums
)
frr = np.concatenate((np.atleast_1d(0), tar_trial_sums / target_scores.size))
far = np.concatenate(
(
np.atleast_1d(1),
nontarget_trial_sums / nontarget_scores.size,
)
)
abs_diffs = np.abs(frr - far)
min_index = np.argmin(abs_diffs)
eer = np.mean((frr[min_index], far[min_index]))
return eer
def main():
weights_path = os.path.join(SERVICE_DIR, "weights", "hfg_aug_1_2.pt")
os.makedirs(os.path.join(SERVICE_DIR, "models"), exist_ok=True)
print("=" * 70)
print("ShiftySpeech (SSL-AASIST) - DeepSafe Dataset Evaluation")
print("=" * 70)
print(f"Weights: {weights_path}")
print(f"Dataset: {DATASET_DIR}")
print(f"Device: cpu")
print()
# Load model
print("Loading model...")
start = time.time()
args_ns = argparse.Namespace()
ssl_model = SSLAASISTModel(args_ns, "cpu")
state_dict = torch.load(weights_path, map_location="cpu", weights_only=False)
ssl_model.load_state_dict(state_dict)
ssl_model.eval()
print(f"Model loaded in {time.time() - start:.1f}s")
print()
# Collect audio files
real_dir = os.path.join(DATASET_DIR, "real")
fake_dir = os.path.join(DATASET_DIR, "fake")
files = []
for fname in sorted(os.listdir(real_dir)):
if fname.endswith(".wav"):
files.append((os.path.join(real_dir, fname), 0, fname))
for fname in sorted(os.listdir(fake_dir)):
if fname.endswith(".wav"):
files.append((os.path.join(fake_dir, fname), 1, fname))
n_real = sum(1 for _, label, _ in files if label == 0)
n_fake = sum(1 for _, label, _ in files if label == 1)
print(f"Total files: {len(files)} (real: {n_real}, fake: {n_fake})")
print()
# Run inference
results = []
total_time = 0.0
print(
f"{'File':<20} {'True':>5} {'Pred':>5} {'P(fake)':>8} "
f"{'P(real)':>8} {'Time':>6}"
)
print("-" * 60)
for path, true_label, fname in files:
audio, sr = librosa.load(path, sr=SAMPLE_RATE, mono=True)
audio = pad_audio(audio)
x = torch.FloatTensor(audio).unsqueeze(0)
t0 = time.time()
with torch.no_grad():
out = ssl_model(x)
elapsed = time.time() - t0
total_time += elapsed
probs = torch.softmax(out, dim=1)
p_fake = probs[0, 0].item()
p_real = probs[0, 1].item()
pred = 1 if p_fake >= 0.5 else 0
results.append(
{
"file": fname,
"true_label": true_label,
"pred_label": pred,
"prob_fake": p_fake,
"prob_real": p_real,
}
)
true_str = "FAKE" if true_label == 1 else "REAL"
pred_str = "FAKE" if pred == 1 else "REAL"
correct = "ok" if pred == true_label else "XX"
print(
f"{fname:<20} {true_str:>5} {pred_str:>5} "
f"{p_fake:>8.4f} {p_real:>8.4f} {elapsed:>5.2f}s "
f"[{correct}]"
)
print()
print("=" * 70)
print("METRICS")
print("=" * 70)
# Compute metrics
true_labels = np.array([r["true_label"] for r in results])
pred_labels = np.array([r["pred_label"] for r in results])
tp = int(np.sum((pred_labels == 1) & (true_labels == 1)))
tn = int(np.sum((pred_labels == 0) & (true_labels == 0)))
fp = int(np.sum((pred_labels == 1) & (true_labels == 0)))
fn = int(np.sum((pred_labels == 0) & (true_labels == 1)))
accuracy = (tp + tn) / len(results) if len(results) > 0 else 0
precision = tp / (tp + fp) if (tp + fp) > 0 else 0
recall = tp / (tp + fn) if (tp + fn) > 0 else 0
f1 = (
2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0
)
specificity = tn / (tn + fp) if (tn + fp) > 0 else 0
# EER using bonafide scores (prob_real: higher = more real)
bonafide_scores = np.array(
[r["prob_real"] for r in results if r["true_label"] == 0]
)
spoof_scores = np.array([r["prob_real"] for r in results if r["true_label"] == 1])
if len(bonafide_scores) > 0 and len(spoof_scores) > 0:
eer = compute_eer(bonafide_scores, spoof_scores)
else:
eer = float("nan")
print(f"Accuracy: {accuracy:.4f} ({accuracy * 100:.1f}%)")
print(f"Precision: {precision:.4f}")
print(f"Recall: {recall:.4f}")
print(f"F1 Score: {f1:.4f}")
print(f"Specificity: {specificity:.4f}")
print(f"EER: {eer:.4f} ({eer * 100:.1f}%)")
print()
print(f"Confusion Matrix:")
print(f" TP={tp:>3d} FP={fp:>3d}")
print(f" FN={fn:>3d} TN={tn:>3d}")
print()
print(f"Total inference time: {total_time:.1f}s")
print(f"Avg per file: {total_time / len(results):.3f}s")
print(f"Total files: {len(results)}")
if __name__ == "__main__":
main()
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