Upload model_wav2vec.py

models/model_wav2vec.py

@@ -0,0 +1,48 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import Wav2Vec2Model
+
+class Wav2VecIntent(nn.Module):
+    def __init__(self, num_classes=31, pretrained_model="facebook/wav2vec2-large"):
+        super().__init__()
+        # Load pretrained wav2vec model
+        self.wav2vec = Wav2Vec2Model.from_pretrained(pretrained_model)
+        
+        # Get hidden size from model config
+        hidden_size = self.wav2vec.config.hidden_size
+        
+        # Add layer normalization
+        self.layer_norm = nn.LayerNorm(hidden_size)
+        
+        # Add attention mechanism
+        self.attention = nn.Linear(hidden_size, 1)
+        
+        # Add dropout for regularization   
+        self.dropout = nn.Dropout(p=0.5)
+        
+        # Classification head
+        self.fc = nn.Linear(hidden_size, num_classes)
+    
+    def forward(self, input_values, attention_mask=None):
+        # Get wav2vec features
+        outputs = self.wav2vec(
+            input_values,
+            attention_mask=attention_mask,
+            return_dict=True
+        )
+        hidden_states = outputs.last_hidden_state  # [batch, sequence, hidden]
+        
+        # Apply layer normalization
+        hidden_states = self.layer_norm(hidden_states)
+        
+        # Apply attention
+        attn_weights = F.softmax(self.attention(hidden_states), dim=1)
+        x = torch.sum(hidden_states * attn_weights, dim=1)  # Weighted sum
+        
+        # Apply dropout
+        x = self.dropout(x)
+        
+        # Final classification
+        x = self.fc(x)
+        return x