File size: 2,489 Bytes
0f96bb5 | 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 | """Test full Nes2Net model on GPU with a real audio sample."""
import sys, os, torch, argparse, numpy as np
PROJECT = r'C:\E\Project\Project B.tech\Multimodal Deepfake Detection'
NES2NET = os.path.join(PROJECT, 'audio_detection', 'Nes2Net_ASVspoof_ITW')
sys.path.insert(0, os.path.join(NES2NET, 'model_scripts'))
sys.path.insert(0, NES2NET)
os.environ['XLSR_CHECKPOINT_PATH'] = os.path.join(PROJECT, 'audio_detection', 'checkpoints', 'xlsr2_300m.pt')
from wav2vec2_Nes2Net_X import wav2vec2_Nes2Net_no_Res_w_allT
# Correct args matching the checkpoint defaults
args = argparse.Namespace(
n_output_logits=2, dilation=2, pool_func='mean',
Nes_ratio=[8, 8], SE_ratio=[1],
)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(f"Using device: {device}")
if device == 'cuda':
print(f" GPU: {torch.cuda.get_device_name(0)}")
print(f" VRAM: {torch.cuda.get_device_properties(0).total_memory / 1024**3:.1f} GB")
print("\nBuilding model...")
model = wav2vec2_Nes2Net_no_Res_w_allT(args=args, device=device)
CKPT_PATH = os.path.join(PROJECT, 'audio_detection', 'checkpoints',
'ASVspoof_2021_wav2vec2_Nes2Net_X_e100_bz12_lr2.5e_07_algo4_avg_ckpt_ep56_60_62_76_95.pth')
print("Loading Nes2Net checkpoint...")
ckpt = torch.load(CKPT_PATH, map_location=device, weights_only=True)
model.load_state_dict(ckpt, strict=False)
model = model.to(device)
model.eval()
print(f"\nModel on device: {next(model.parameters()).device}")
# Test with synthetic audio (2 seconds at 16kHz)
print("\n=== Test 1: Random noise (should lean toward fake) ===")
x = torch.randn(1, 32000).to(device)
with torch.no_grad():
out = model(x)
score = out[0][0].item() - out[0][1].item()
print(f" Raw output: {out[0].cpu().tolist()}")
print(f" Score (real-fake): {score:.4f}")
print(f" Prediction: {'Real' if score > 0 else 'Fake'}")
# Test with a sine wave (440Hz, 2 seconds)
print("\n=== Test 2: 440Hz sine wave ===")
t = torch.linspace(0, 2, 32000).to(device)
sine = (torch.sin(2 * 3.14159 * 440 * t) * 0.5).unsqueeze(0)
with torch.no_grad():
out = model(sine)
score = out[0][0].item() - out[0][1].item()
print(f" Raw output: {out[0].cpu().tolist()}")
print(f" Score (real-fake): {score:.4f}")
print(f" Prediction: {'Real' if score > 0 else 'Fake'}")
if device == 'cuda':
print(f"\nGPU memory used: {torch.cuda.memory_allocated() / 1024**3:.2f} GB")
print(f"GPU memory cached: {torch.cuda.memory_reserved() / 1024**3:.2f} GB")
print("\nGPU inference test PASSED!")
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