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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 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 | from __future__ import annotations
import argparse
import json
from pathlib import Path
from typing import Dict
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 .model_aasistpp import AuralGuardAASISTPP, compute_multitask_loss
from .metrics import classification_report_dict
def parse_args():
p = argparse.ArgumentParser(description="Train AuralGuard-AASIST++")
p.add_argument("--train-csv", required=True)
p.add_argument("--val-csv", required=True)
p.add_argument("--aasist-root", default="external/aasist")
p.add_argument("--aasist-config", default="external/aasist/config/AASIST.conf")
p.add_argument("--aasist-checkpoint", default=None, help="Optional pretrained AASIST checkpoint")
p.add_argument("--out-dir", default="results/run1")
p.add_argument("--epochs", type=int, default=10)
p.add_argument("--batch-size", type=int, default=8)
p.add_argument("--lr", type=float, default=1e-4)
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("--num-workers", type=int, default=2)
p.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu")
p.add_argument("--freeze-backbone", action="store_true")
return p.parse_args()
def move_batch_to_device(batch: Dict, device: torch.device) -> Dict:
out = {}
for k, v in batch.items():
out[k] = v.to(device) if torch.is_tensor(v) else v
return out
def run_epoch(model, loader, optimizer, device, train: bool):
model.train(train)
total_loss = 0.0
y_true, y_pred, fake_scores = [], [], []
attack_true, attack_pred = [], []
exp_true, exp_pred = [], []
loop = tqdm(loader, desc="train" if train else "val", leave=False)
for batch in loop:
batch = move_batch_to_device(batch, device)
with torch.set_grad_enabled(train):
outputs = model(batch["wav"], freq_aug=train)
losses = compute_multitask_loss(
outputs,
batch["binary_label"],
batch["attack_label"],
batch["explanation_label"],
)
if train:
optimizer.zero_grad(set_to_none=True)
losses["loss"].backward()
optimizer.step()
probs = torch.softmax(outputs["binary_logits"], dim=-1)
fake_prob = probs[:, 1]
pred = torch.argmax(outputs["binary_logits"], dim=-1)
attack_p = torch.argmax(outputs["attack_logits"], dim=-1)
exp_p = torch.argmax(outputs["explanation_logits"], dim=-1)
bs = batch["wav"].shape[0]
total_loss += losses["loss"].item() * bs
y_true.extend(batch["binary_label"].detach().cpu().tolist())
y_pred.extend(pred.detach().cpu().tolist())
fake_scores.extend(fake_prob.detach().cpu().tolist())
attack_true.extend(batch["attack_label"].detach().cpu().tolist())
attack_pred.extend(attack_p.detach().cpu().tolist())
exp_true.extend(batch["explanation_label"].detach().cpu().tolist())
exp_pred.extend(exp_p.detach().cpu().tolist())
loop.set_postfix(loss=losses["loss"].item())
metrics = classification_report_dict(
y_true, y_pred, fake_scores, attack_true, attack_pred, exp_true, exp_pred
)
metrics["loss"] = total_loss / max(len(loader.dataset), 1)
return metrics
def main():
args = parse_args()
out_dir = Path(args.out_dir)
out_dir.mkdir(parents=True, exist_ok=True)
device = torch.device(args.device)
audio_train = AudioConfig(args.sample_rate, args.duration_sec, random_crop=True)
audio_eval = AudioConfig(args.sample_rate, args.duration_sec, random_crop=False)
train_ds = AuralGuardDataset(args.train_csv, audio_config=audio_train)
val_ds = AuralGuardDataset(args.val_csv, audio_config=audio_eval)
train_loader = DataLoader(
train_ds, batch_size=args.batch_size, shuffle=True,
num_workers=args.num_workers, pin_memory=(device.type == "cuda")
)
val_loader = DataLoader(
val_ds, batch_size=args.batch_size, shuffle=False,
num_workers=args.num_workers, pin_memory=(device.type == "cuda")
)
backbone = build_aasist_backbone(
args.aasist_root, args.aasist_config, checkpoint=args.aasist_checkpoint, device=device
)
model = AuralGuardAASISTPP(
backbone,
feature_dim=args.feature_dim,
freeze_backbone=args.freeze_backbone,
).to(device)
optimizer = torch.optim.AdamW(filter(lambda p: p.requires_grad, model.parameters()), lr=args.lr)
best_eer = float("inf")
history = []
for epoch in range(1, args.epochs + 1):
print(f"\nEpoch {epoch}/{args.epochs}")
train_metrics = run_epoch(model, train_loader, optimizer, device, train=True)
val_metrics = run_epoch(model, val_loader, optimizer, device, train=False)
record = {"epoch": epoch, "train": train_metrics, "val": val_metrics}
history.append(record)
print("train:", json.dumps(train_metrics, indent=2))
print("val:", json.dumps(val_metrics, indent=2))
ckpt = {
"epoch": epoch,
"model": model.state_dict(),
"args": vars(args),
"val_metrics": val_metrics,
}
torch.save(ckpt, out_dir / "last.pt")
val_eer = val_metrics.get("eer", float("inf"))
if val_eer < best_eer:
best_eer = val_eer
torch.save(ckpt, out_dir / "best.pt")
print(f"Saved new best checkpoint with EER={best_eer:.4f}")
with (out_dir / "history.json").open("w", encoding="utf-8") as f:
json.dump(history, f, indent=2)
print(f"Done. Best checkpoint: {out_dir / 'best.pt'}")
if __name__ == "__main__":
main()
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