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Running on Zero
Running on Zero
| from __future__ import annotations | |
| import sys | |
| from pathlib import Path | |
| PROJECT_ROOT = Path(__file__).resolve().parents[1] | |
| sys.path.insert(0, str(PROJECT_ROOT)) | |
| import argparse | |
| from pathlib import Path | |
| from types import SimpleNamespace | |
| import pandas as pd | |
| import torch | |
| from tqdm import tqdm | |
| from src.infer import load_model, run_auralguard | |
| from src.audio_quality import audio_quality_report | |
| from src.decision_gate import make_safe_report | |
| def parse_args(): | |
| p = argparse.ArgumentParser(description="Evaluate false fake alarms on real/bonafide datasets.") | |
| p.add_argument("--csv", required=True, help="Metadata CSV containing real/bonafide rows") | |
| 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("--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("--device", default="cuda" if torch.cuda.is_available() else "cpu") | |
| p.add_argument("--limit", type=int, default=0) | |
| p.add_argument("--out-csv", required=True) | |
| return p.parse_args() | |
| def main(): | |
| args = parse_args() | |
| df = pd.read_csv(args.csv, low_memory=False) | |
| df = df[df["binary_label"].astype(int) == 0].reset_index(drop=True) | |
| if args.limit and args.limit > 0: | |
| df = df.head(args.limit) | |
| device = torch.device(args.device) | |
| model_args = SimpleNamespace(**vars(args)) | |
| model = load_model(model_args, device) | |
| rows = [] | |
| for _, row in tqdm(df.iterrows(), total=len(df), desc="False-alarm eval"): | |
| path = row["file_path"] | |
| report = run_auralguard( | |
| path, | |
| model, | |
| sample_rate=args.sample_rate, | |
| duration_sec=args.duration_sec, | |
| device=device, | |
| ) | |
| quality = audio_quality_report(path, target_sr=args.sample_rate) | |
| safe = make_safe_report(report, quality_warnings=quality["warnings"], ood_warning=False) | |
| fake_prob = float(report.get("fake_probability", 0.0)) | |
| rows.append({ | |
| "file_path": path, | |
| "dataset": row.get("dataset", ""), | |
| "original_decision": report.get("decision", ""), | |
| "safe_decision": safe.get("decision", ""), | |
| "fake_probability": fake_prob, | |
| "attack_type": report.get("attack_type", ""), | |
| "is_false_fake_original_065": int(fake_prob >= 0.65), | |
| "is_false_fake_strict_085": int(fake_prob >= 0.85), | |
| "review_required": int(safe.get("review_required", False)), | |
| "quality_warnings": " | ".join(quality["warnings"]), | |
| }) | |
| out = pd.DataFrame(rows) | |
| Path(args.out_csv).parent.mkdir(parents=True, exist_ok=True) | |
| out.to_csv(args.out_csv, index=False) | |
| print("\nSaved:", args.out_csv) | |
| print("Rows:", len(out)) | |
| if len(out): | |
| print("False fake rate, threshold 0.65:", round(out["is_false_fake_original_065"].mean() * 100, 2), "%") | |
| print("False fake rate, threshold 0.85:", round(out["is_false_fake_strict_085"].mean() * 100, 2), "%") | |
| print("\nSafe decision counts:") | |
| print(out["safe_decision"].value_counts()) | |
| if __name__ == "__main__": | |
| main() | |