Create pipeline.py

pipeline.py

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+import torch
+import joblib
+import librosa
+import numpy as np
+from torch import nn
+from transformers import AutoModel
+
+class VoiceRecognitionModel(nn.Module):
+    def __init__(self, num_classes):
+        super().__init__()
+        # Your model architecture here (same as training)
+        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)
+        # ... rest of your architecture
+        
+    def forward(self, x):
+        # Your forward pass
+        return x
+
+def extract_features(file_path, max_pad_len=174):
+    # Your feature extraction code
+    pass
+
+def pipeline():
+    # This will be called when someone uses your model
+    model = VoiceRecognitionModel(num_classes=7)  # Adjust based on your classes
+    model.load_state_dict(torch.load("voice_recognition_model.pth"))
+    model.eval()
+    
+    label_encoder = joblib.load("label_encoder.joblib")
+    feature_params = joblib.load("feature_params.joblib")
+    
+    return model, label_encoder, feature_params