diff --git a/vllm/model_executor/models/whisper.py b/vllm/model_executor/models/whisper.py
index be4b123d60f7f..fe87be4665cf9 100644
--- a/vllm/model_executor/models/whisper.py
+++ b/vllm/model_executor/models/whisper.py
@@ -24,8 +24,8 @@ from vllm.attention.backends.abstract import (
 from vllm.attention.layer import Attention
 from vllm.attention.layers.cross_attention import CrossAttention
 from vllm.attention.layers.mm_encoder_attention import MMEncoderAttention
-from vllm.config import CacheConfig, ModelConfig, SpeechToTextConfig, VllmConfig
 from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, ModelConfig, SpeechToTextConfig, VllmConfig
 from vllm.config.multimodal import BaseDummyOptions
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.inputs.data import PromptType
@@ -106,9 +106,6 @@ class WhisperEncoderAttention(MMEncoderAttention):
 
     def __init__(self, num_heads: int, head_size: int, scale: float, num_kv_heads: int):
         super().__init__(num_heads, head_size, scale, num_kv_heads)
-        self.num_heads = num_heads
-        self.head_size = head_size
-        self.num_kv_heads = num_kv_heads
 
     def forward(
         self,
@@ -193,9 +190,9 @@ class WhisperAttention(nn.Module):
         )
         if attn_type == AttentionType.ENCODER:
             self.attn = WhisperEncoderAttention(
-                num_heads=self.num_heads,
-                head_size=self.head_dim,
-                scale=self.scaling,
+                self.num_heads,
+                self.head_dim,
+                self.scaling,
                 num_kv_heads=self.num_kv_heads,
             )
         elif self.attn_type == AttentionType.ENCODER_DECODER: