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Running on Zero
Running on Zero
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127b976 | 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 | from __future__ import annotations
import argparse
import json
from pathlib import Path
import pandas as pd
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from .aasist_loader import build_aasist_backbone
from .dataset import AuralGuardDataset, AudioConfig
from .labels import ID_TO_ATTACK, ID_TO_EXPLANATION
from .metrics import classification_report_dict, make_confusion_matrix
from .model_aasistpp import AuralGuardAASISTPP
def parse_args():
p = argparse.ArgumentParser(description="Evaluate AuralGuard-AASIST++")
p.add_argument("--csv", required=True)
p.add_argument("--checkpoint", required=True)
p.add_argument("--aasist-root", default="external/aasist")
p.add_argument("--aasist-config", default="external/aasist/config/AASIST.conf")
p.add_argument("--out-csv", default="results/predictions.csv")
p.add_argument("--sample-rate", type=int, default=16000)
p.add_argument("--duration-sec", type=float, default=4.0)
p.add_argument("--feature-dim", type=int, default=160)
p.add_argument("--batch-size", type=int, default=8)
p.add_argument("--num-workers", type=int, default=2)
p.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu")
return p.parse_args()
def main():
args = parse_args()
device = torch.device(args.device)
ds = AuralGuardDataset(args.csv, audio_config=AudioConfig(args.sample_rate, args.duration_sec))
loader = DataLoader(ds, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers)
backbone = build_aasist_backbone(args.aasist_root, args.aasist_config, device=device)
model = AuralGuardAASISTPP(backbone, feature_dim=args.feature_dim).to(device)
ckpt = torch.load(args.checkpoint, map_location=device)
model.load_state_dict(ckpt["model"] if "model" in ckpt else ckpt, strict=True)
model.eval()
rows = []
with torch.no_grad():
for batch in tqdm(loader, desc="evaluate"):
wav = batch["wav"].to(device)
outputs = model(wav)
bin_probs = torch.softmax(outputs["binary_logits"], dim=-1)
fake_scores = bin_probs[:, 1]
bin_pred = torch.argmax(outputs["binary_logits"], dim=-1)
attack_pred = torch.argmax(outputs["attack_logits"], dim=-1)
exp_pred = torch.argmax(outputs["explanation_logits"], dim=-1)
for i in range(wav.shape[0]):
rows.append({
"file_path": batch["file_path"][i],
"true_binary": int(batch["binary_label"][i]),
"pred_binary": int(bin_pred[i].cpu()),
"fake_probability": float(fake_scores[i].cpu()),
"true_attack": int(batch["attack_label"][i]),
"pred_attack": int(attack_pred[i].cpu()),
"pred_attack_name": ID_TO_ATTACK[int(attack_pred[i].cpu())],
"true_explanation": int(batch["explanation_label"][i]),
"pred_explanation": int(exp_pred[i].cpu()),
"pred_explanation_name": ID_TO_EXPLANATION[int(exp_pred[i].cpu())],
})
pred_df = pd.DataFrame(rows)
out_csv = Path(args.out_csv)
out_csv.parent.mkdir(parents=True, exist_ok=True)
pred_df.to_csv(out_csv, index=False)
metrics = classification_report_dict(
pred_df["true_binary"], pred_df["pred_binary"], pred_df["fake_probability"],
pred_df["true_attack"], pred_df["pred_attack"],
pred_df["true_explanation"], pred_df["pred_explanation"],
)
metrics["attack_confusion_matrix"] = make_confusion_matrix(pred_df["true_attack"], pred_df["pred_attack"])
metrics_path = out_csv.with_suffix(".metrics.json")
with metrics_path.open("w", encoding="utf-8") as f:
json.dump(metrics, f, indent=2)
print(json.dumps(metrics, indent=2))
print(f"Saved predictions to {out_csv}")
print(f"Saved metrics to {metrics_path}")
if __name__ == "__main__":
main()
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