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| from __future__ import annotations | |
| import argparse | |
| from pathlib import Path | |
| from types import SimpleNamespace | |
| import pandas as pd | |
| import torch | |
| from src.infer import load_model, run_auralguard | |
| from src.audio_quality import audio_quality_report | |
| from src.decision_gate import make_safe_report | |
| from src.explain_plus import beginner_friendly_explanation_plus | |
| def parse_args(): | |
| p = argparse.ArgumentParser(description="Compare multiple checkpoints on the same audio clip.") | |
| p.add_argument("--audio", required=True) | |
| p.add_argument("--names", nargs="+", required=True) | |
| p.add_argument("--checkpoints", nargs="+", 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("--out-csv", required=True) | |
| return p.parse_args() | |
| def main(): | |
| args = parse_args() | |
| if len(args.names) != len(args.checkpoints): | |
| raise ValueError("--names and --checkpoints must have the same length") | |
| device = torch.device(args.device) | |
| quality = audio_quality_report(args.audio, args.sample_rate) | |
| rows = [] | |
| for name, ckpt in zip(args.names, args.checkpoints): | |
| model_args = SimpleNamespace(**vars(args)) | |
| model_args.checkpoint = ckpt | |
| model = load_model(model_args, device) | |
| report = run_auralguard( | |
| args.audio, | |
| model, | |
| sample_rate=args.sample_rate, | |
| duration_sec=args.duration_sec, | |
| device=device, | |
| ) | |
| safe = make_safe_report(report, quality_warnings=quality["warnings"], ood_warning=False) | |
| gate = safe.get("decision_gate", {}) | |
| beginner = beginner_friendly_explanation_plus( | |
| fake_prob=float(report.get("fake_probability", 0.0)), | |
| attack_type=report.get("attack_type", "unknown"), | |
| suspicious_segments=report.get("suspicious_segments", []), | |
| safe_decision=safe.get("decision", ""), | |
| warnings=gate.get("warnings", []), | |
| ) | |
| rows.append({ | |
| "model": name, | |
| "checkpoint": ckpt, | |
| "original_decision": report.get("original_decision", report.get("decision", "")), | |
| "safe_decision": safe.get("decision", ""), | |
| "fake_probability": float(report.get("fake_probability", 0.0)), | |
| "attack_type": report.get("attack_type", ""), | |
| "review_required": safe.get("review_required", False), | |
| "beginner_explanation": beginner, | |
| "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(out[["model", "safe_decision", "fake_probability", "attack_type", "review_required"]]) | |
| print("\nSaved:", args.out_csv) | |
| if __name__ == "__main__": | |
| main() | |