Delete emotion_model.py

emotion_model.py

@@ -1,52 +0,0 @@
-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')