Claude Opus 4.6
feat: implement fairseq compatibility shim and working audio inference pipeline
0f96bb5 | """Test full Nes2Net model loading and inference.""" | |
| import sys, os, torch, argparse | |
| # Setup paths | |
| 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 | |
| # Setup args matching the Nes2Net-X config (defaults from easy_inference_demo.py) | |
| args = argparse.Namespace( | |
| n_output_logits=2, | |
| dilation=2, | |
| pool_func='mean', | |
| Nes_ratio=[8, 8], | |
| SE_ratio=[1], | |
| ) | |
| print("Building full Nes2Net model...") | |
| model = wav2vec2_Nes2Net_no_Res_w_allT(args=args, device='cpu') | |
| # Load the Nes2Net checkpoint | |
| 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(f"\nLoading Nes2Net checkpoint...") | |
| ckpt = torch.load(CKPT_PATH, map_location='cpu', weights_only=True) | |
| # Map checkpoint keys: checkpoint has ssl_model.model.* and Nested_Res2Net_TDNN.* | |
| print(f"Checkpoint has {len(ckpt)} keys") | |
| missing, unexpected = model.load_state_dict(ckpt, strict=False) | |
| print(f"Missing keys: {len(missing)}") | |
| if missing: | |
| for k in missing[:10]: | |
| print(f" {k}") | |
| print(f"Unexpected keys: {len(unexpected)}") | |
| if unexpected: | |
| for k in unexpected[:10]: | |
| print(f" {k}") | |
| # Test forward pass | |
| print("\nTesting forward pass...") | |
| x = torch.randn(1, 32000) # 2 seconds of audio at 16kHz | |
| model.eval() | |
| with torch.no_grad(): | |
| out = model(x) | |
| print(f"Output shape: {out.shape}") | |
| print(f"Output values: {out}") | |
| print(f"Prediction: {'Real' if out[0][0] > out[0][1] else 'Fake'}") | |
| print("\nFull pipeline test PASSED!") | |