diff --git a/docs/source/bettertransformer/overview.mdx b/docs/source/bettertransformer/overview.mdx
index 465ed014934..19b56275b17 100644
--- a/docs/source/bettertransformer/overview.mdx
+++ b/docs/source/bettertransformer/overview.mdx
@@ -24,6 +24,7 @@ You can now use this feature in 🤗 Optimum together with Transformers and use
 The list of supported model below:
 
 - [AlBERT](https://arxiv.org/abs/1909.11942)
+- [ASTLayer](https://arxiv.org/abs/2104.01778)
 - [BART](https://arxiv.org/abs/1910.13461)
 - [BERT](https://arxiv.org/abs/1810.04805)
 - [BERT-generation](https://arxiv.org/abs/1907.12461)
diff --git a/optimum/bettertransformer/models/__init__.py b/optimum/bettertransformer/models/__init__.py
index f34766a2e43..5df1dffdfe7 100644
--- a/optimum/bettertransformer/models/__init__.py
+++ b/optimum/bettertransformer/models/__init__.py
@@ -15,6 +15,7 @@
 
 from .encoder_models import (
     AlbertLayerBetterTransformer,
+    ASTLayerBetterTransformer,
     BartEncoderLayerBetterTransformer,
     BertLayerBetterTransformer,
     CLIPLayerBetterTransformer,
@@ -30,6 +31,7 @@
 
 class BetterTransformerManager:
     MODEL_MAPPING = {
+        "audio-spectrogram-transformer": ("ASTLayer", ASTLayerBetterTransformer),
         "albert": ("AlbertLayer", AlbertLayerBetterTransformer),
         "bart": ("BartEncoderLayer", BartEncoderLayerBetterTransformer),
         "bert": ("BertLayer", BertLayerBetterTransformer),
diff --git a/optimum/bettertransformer/models/encoder_models.py b/optimum/bettertransformer/models/encoder_models.py
index df3b81f4374..6c5cd15e100 100644
--- a/optimum/bettertransformer/models/encoder_models.py
+++ b/optimum/bettertransformer/models/encoder_models.py
@@ -128,6 +128,100 @@ def forward(self, hidden_states, attention_mask, *_):
         return (hidden_states,)
 
 
+class ASTLayerBetterTransformer(BetterTransformerBaseLayer):
+    def __init__(self, ast_layer, config):
+        r"""
+        A simple conversion of the `ASTLayer` to its `BetterTransformer` implementation.
+        Args:
+            ast_layer (`torch.nn.Module`):
+                The original `ASTLayer` where the weights needs to be retrieved.
+        """
+        super().__init__(config)
+        # In_proj layer
+        self.in_proj_weight = nn.Parameter(
+            torch.cat(
+                [
+                    ast_layer.attention.attention.query.weight,
+                    ast_layer.attention.attention.key.weight,
+                    ast_layer.attention.attention.value.weight,
+                ]
+            )
+        )
+
+        self.in_proj_bias = nn.Parameter(
+            torch.cat(
+                [
+                    ast_layer.attention.attention.query.bias,
+                    ast_layer.attention.attention.key.bias,
+                    ast_layer.attention.attention.value.bias,
+                ]
+            )
+        )
+
+        # Out proj layer
+        self.out_proj_weight = ast_layer.attention.output.dense.weight
+        self.out_proj_bias = ast_layer.attention.output.dense.bias
+
+        # Linear layer 1
+        self.linear1_weight = ast_layer.intermediate.dense.weight
+        self.linear1_bias = ast_layer.intermediate.dense.bias
+
+        # Linear layer 2
+        self.linear2_weight = ast_layer.output.dense.weight
+        self.linear2_bias = ast_layer.output.dense.bias
+
+        # Layer norm 1
+        self.norm1_eps = ast_layer.layernorm_before.eps
+        self.norm1_weight = ast_layer.layernorm_before.weight
+        self.norm1_bias = ast_layer.layernorm_before.bias
+
+        # Layer norm 2
+        self.norm2_eps = ast_layer.layernorm_after.eps
+        self.norm2_weight = ast_layer.layernorm_after.weight
+        self.norm2_bias = ast_layer.layernorm_after.bias
+
+        # Model hyper parameters
+        self.num_heads = ast_layer.attention.attention.num_attention_heads
+        self.embed_dim = int(ast_layer.attention.attention.attention_head_size * self.num_heads)
+
+        # Last step: set the last layer to `False` -> this will be set to `True` when converting the model
+        self.is_last_layer = False
+        self.norm_first = True
+        self.validate_bettertransformer()
+
+    def forward(self, hidden_states, *_, **__):
+        r"""
+        This is just a wrapper around the forward function proposed in:
+        https://github.com/huggingface/transformers/pull/19553
+        """
+        super().forward_checker()
+        attention_mask = None
+        hidden_states = torch._transformer_encoder_layer_fwd(
+            hidden_states,
+            self.embed_dim,
+            self.num_heads,
+            self.in_proj_weight,
+            self.in_proj_bias,
+            self.out_proj_weight,
+            self.out_proj_bias,
+            self.use_gelu,
+            self.norm_first,
+            self.norm1_eps,
+            self.norm1_weight,
+            self.norm1_bias,
+            self.norm2_weight,
+            self.norm2_bias,
+            self.linear1_weight,
+            self.linear1_bias,
+            self.linear2_weight,
+            self.linear2_bias,
+            attention_mask,
+        )
+        if hidden_states.is_nested and self.is_last_layer:
+            hidden_states = hidden_states.to_padded_tensor(0.0)
+        return (hidden_states,)
+
+
 class BertLayerBetterTransformer(BetterTransformerBaseLayer):
     def __init__(self, bert_layer, config):
         r"""
diff --git a/tests/bettertransformer/test_bettertransformer_audio.py b/tests/bettertransformer/test_bettertransformer_audio.py
index 563bd4d8202..143677ce728 100644
--- a/tests/bettertransformer/test_bettertransformer_audio.py
+++ b/tests/bettertransformer/test_bettertransformer_audio.py
@@ -28,6 +28,10 @@
     "ybelkada/hubert-tiny-random",
 ]
 
+AST_TO_TEST = [
+    "Ericwang/tiny-random-ast",
+]
+
 
 class BetterTransformersWhisperTest(BetterTransformersTestMixin, unittest.TestCase):
     r"""
@@ -56,6 +60,24 @@ def prepare_inputs_for_class(self, model_id):
         return input_dict
 
 
+class BetterTransformersASTTest(BetterTransformersTestMixin, unittest.TestCase):
+    r"""
+    Testing suite for AST - tests all the tests defined in `BetterTransformersTestMixin`
+    Since `AST` uses slightly different preprocessor than other audio models, it is preferrable
+    to define its own testing class.
+    """
+    all_models_to_test = AST_TO_TEST
+
+    def prepare_inputs_for_class(self, model_id):
+        batch_duration_in_seconds = [1, 3, 2, 6]
+        input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds]
+
+        feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
+
+        input_dict = feature_extractor(input_features, return_tensors="pt", padding=True)
+        return input_dict
+
+
 class BetterTransformersAudioTest(BetterTransformersTestMixin, unittest.TestCase):
     r"""
     Testing suite for Audio models - tests all the tests defined in `BetterTransformersTestMixin`