diff --git a/stable_audio_tools/models/adp.py b/stable_audio_tools/models/adp.py
index 3041446..7422779 100644
--- a/stable_audio_tools/models/adp.py
+++ b/stable_audio_tools/models/adp.py
@@ -77,7 +77,7 @@ def ConvTranspose1d(*args, **kwargs) -> nn.Module:
 def Downsample1d(
     in_channels: int, out_channels: int, factor: int, kernel_multiplier: int = 2
 ) -> nn.Module:
-    assert kernel_multiplier % 2 == 0, "Kernel multiplier must be even"
+    assert kernel_multiplier % 2 == 0, "Kernel multiplier should be divisible by 2."
 
     return Conv1d(
         in_channels=in_channels,
@@ -217,7 +217,8 @@ def __init__(
         )
 
         if self.use_mapping:
-            assert exists(context_mapping_features)
+            assert_message = "Ensure that context mapping features exist when use_mapping is set to True."
+            assert exists(context_mapping_features), assert_message
             self.to_scale_shift = MappingToScaleShift(
                 features=context_mapping_features, channels=out_channels
             )
@@ -581,7 +582,7 @@ class LearnedPositionalEmbedding(nn.Module):
 
     def __init__(self, dim: int):
         super().__init__()
-        assert (dim % 2) == 0
+        assert (dim % 2) == 0, "Ensure that the value of 'dim' can be divided by 2."
         half_dim = dim // 2
         self.weights = nn.Parameter(torch.randn(half_dim))
 
@@ -657,10 +658,11 @@ def __init__(
         )
 
         if self.use_transformer:
+            assert_message = "Both attention_heads and attention_multiplier must be specified when using transformer."
             assert (
                 exists(attention_heads)
                 and exists(attention_multiplier)
-            )
+            ),  assert_message
 
             attention_features = default(attention_features, channels // attention_heads)
 
@@ -765,10 +767,11 @@ def __init__(
         )
 
         if self.use_transformer:
+            assert_message = "Both attention_heads and attention_multiplier must be specified when using transformer."
             assert (
                 exists(attention_heads)
                 and exists(attention_multiplier)
-            )
+            ),  assert_message
 
             attention_features = default(attention_features, channels // attention_heads)
 
@@ -857,10 +860,11 @@ def __init__(
         )
 
         if self.use_transformer:
+            assert_message = "Both attention_heads and attention_multiplier must be specified when using transformer."
             assert (
                 exists(attention_heads)
                 and exists(attention_multiplier)
-            )
+            ), assert_message
 
             attention_features = default(attention_features, channels // attention_heads)
 
@@ -972,7 +976,8 @@ def __init__(
             )
 
         if use_context_time:
-            assert exists(context_mapping_features)
+            assert_message = "When use_context_time is set to True, context_mapping_features must be also specified."
+            assert exists(context_mapping_features), assert_message
             self.to_time = nn.Sequential(
                 TimePositionalEmbedding(
                     dim=channels, out_features=context_mapping_features
@@ -981,7 +986,8 @@ def __init__(
             )
 
         if use_context_features:
-            assert exists(context_features) and exists(context_mapping_features)
+            assert_message = "When use_context_features is set to True, context_feature and context_mapping_features must be both specified."
+            assert exists(context_features) and exists(context_mapping_features), assert_message
             self.to_features = nn.Sequential(
                 nn.Linear(
                     in_features=context_features, out_features=context_mapping_features