Create emotion_model.py

emotion_model.py

@@ -0,0 +1,52 @@
+import tensorflow as tf
+import torch
+import numpy as np
+import os
+
+# Load the Keras model
+keras_model = tf.keras.models.load_model('wav2vec_model.h5')
+
+# Create a PyTorch model with the same architecture
+class EmotionClassifier(torch.nn.Module):
+    def __init__(self, input_shape, num_classes):
+        super().__init__()
+        # Adjust this architecture to match your Keras model
+        self.flatten = torch.nn.Flatten()
+        self.layers = torch.nn.Sequential(
+            torch.nn.Linear(input_shape, 128),
+            torch.nn.ReLU(),
+            torch.nn.Dropout(0.3),
+            torch.nn.Linear(128, 64),
+            torch.nn.ReLU(),
+            torch.nn.Dropout(0.3),
+            torch.nn.Linear(64, num_classes)
+        )
+        
+    def forward(self, x):
+        x = self.flatten(x)
+        return self.layers(x)
+
+# Create PyTorch model
+# Adjust these parameters based on your Keras model
+input_shape = 13 * 128  # n_mfcc * max_length
+num_classes = 7  # Number of emotions
+pytorch_model = EmotionClassifier(input_shape, num_classes)
+
+# Copy weights from Keras to PyTorch
+# This would need to be adjusted based on your exact architecture
+for i, layer in enumerate(keras_model.layers):
+    if isinstance(layer, tf.keras.layers.Dense):
+        # Get Keras weights and bias
+        keras_weights = layer.get_weights()[0]
+        keras_bias = layer.get_weights()[1]
+        
+        # Find the corresponding PyTorch layer
+        # This is simplified; you'd need to match layers properly
+        pytorch_layer = pytorch_model.layers[i * 2]
+        
+        # Copy weights and bias
+        pytorch_layer.weight.data = torch.tensor(keras_weights.T, dtype=torch.float32)
+        pytorch_layer.bias.data = torch.tensor(keras_bias, dtype=torch.float32)
+
+# Save the PyTorch model
+torch.save(pytorch_model.state_dict(), 'emotion_model.pt')