f

app.py

@@ -74,18 +74,17 @@ class AgeGenderModel(Wav2Vec2PreTrainedModel):
 # Fusion = AgeWav2Vec2Model forward() will accept already computed CNN7 features from ExpressioNmodel forward()
 def _forward(
     self,
-    extract_features,
+    frozen_cnn7=None,  # CNN7 fetures of wav2vec2 calc. from CNN7 feature extractor (once)
     attention_mask=None):
-    # extract_features : CNN7 fetures of wav2vec2 as they are calc. from CNN7 feature extractor
 
 
     if attention_mask is not None:
         # compute reduced attention_mask corresponding to feature vectors
         attention_mask = self._get_feature_vector_attention_mask(
-            extract_features.shape[1], attention_mask, add_adapter=False
+            frozen_cnn7.shape[1], attention_mask, add_adapter=False
         )
 
-    hidden_states, extract_features = self.feature_projection(extract_features)
+    hidden_states, _ = self.feature_projection(frozen_cnn7)
     hidden_states = self._mask_hidden_states(
         hidden_states, mask_time_indices=mask_time_indices, attention_mask=attention_mask
     )
@@ -121,7 +120,7 @@ def _forward_and_cnn7(
             frozen_cnn7.shape[1], attention_mask, add_adapter=False
         )
 
-    hidden_states, extract_features = self.feature_projection(frozen_cnn7)  # grad=True non frozen
+    hidden_states, _ = self.feature_projection(frozen_cnn7)  # grad=True non frozen
     hidden_states = self._mask_hidden_states(
         hidden_states, mask_time_indices=mask_time_indices, attention_mask=attention_mask
     )