diff --git a/models/modeling_flax_wav2vec2.py b/models/modeling_flax_wav2vec2.py
index 6095cd0..bc878d5 100644
--- a/models/modeling_flax_wav2vec2.py
+++ b/models/modeling_flax_wav2vec2.py
@@ -694,7 +694,7 @@ def __call__(
     def _get_feat_extract_output_lengths(
         self, input_lengths: Union[jnp.ndarray, int], add_adapter: Optional[bool] = None
     ):
-        return self.module._get_feat_extract_output_lengths(input_lengths)
+        return self.module._get_feat_extract_output_lengths(input_lengths, add_adapter=add_adapter)
 
 
 class FlaxWav2Vec2Module(nn.Module):
@@ -730,15 +730,10 @@ def __call__(
 
         # make sure that no loss is computed on padded inputs
         if attention_mask is not None:
-            # compute real output lengths according to convolution formula
-            output_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1).astype("i4"))
-
-            attention_mask = jnp.zeros(extract_features.shape[:2], dtype=self.dtype)
-
-            # these two operations makes sure that all values
-            # before the output lengths indices are attended to
-            attention_mask = attention_mask.at[jnp.arange(attention_mask.shape[0]), output_lengths - 1].set(1)
-            attention_mask = jnp.flip(jnp.flip(attention_mask, -1).cumsum(-1), -1).astype("bool")
+            # 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
+            )
 
         hidden_states, extract_features = self.feature_projection(extract_features, deterministic=deterministic)
         if mask_time_indices is not None:  # apply SpecAugment along time axis with given indices
@@ -808,12 +803,10 @@ def _get_feature_vector_attention_mask(
         batch_size = attention_mask.shape[0]
 
         attention_mask = jnp.zeros((batch_size, feature_vector_length), dtype=attention_mask.dtype)
-        # these two operations makes sure that all values before the output lengths idxs are attended to
-        idx = (jnp.arange(attention_mask.shape[0]), output_lengths - 1)
-        attention_mask = attention_mask.at[idx].set(1)
-        attention_mask = jnp.flip(jnp.flip(attention_mask, axis=-1).cumsum(axis=-1), axis=-1)
-
-        attention_mask = jnp.array(attention_mask, dtype=bool)
+        # these two operations makes sure that all values
+        # before the output lengths indices are attended to
+        attention_mask = attention_mask.at[jnp.arange(attention_mask.shape[0]), output_lengths - 1].set(1)
+        attention_mask = jnp.flip(jnp.flip(attention_mask, -1).cumsum(-1), -1).astype("bool")
         return attention_mask