diff --git a/vllm/model_executor/models/qwen3_omni_moe_thinker.py b/vllm/model_executor/models/qwen3_omni_moe_thinker.py
index d565a0108432..d5a75e75aa43 100755
--- a/vllm/model_executor/models/qwen3_omni_moe_thinker.py
+++ b/vllm/model_executor/models/qwen3_omni_moe_thinker.py
@@ -30,7 +30,9 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+from packaging.version import Version
 from transformers import PretrainedConfig
+from transformers import __version__ as TRANSFORMERS_VERSION
 from transformers.feature_extraction_utils import BatchFeature
 from transformers.models.qwen3_omni_moe.configuration_qwen3_omni_moe import (
     Qwen3OmniMoeConfig,
@@ -711,11 +713,12 @@ def pad_to_hop_length(x: np.ndarray, hop_length: int) -> np.ndarray:
             return x
 
         # NOTE: WhisperFeatureExtractor cannot handle empty list of audios
+        feature_extractor = self.info.get_feature_extractor()
+        hop_length = feature_extractor.hop_length
         if audios:
             # NOTE: Qwen3-Omni processor accept "audio"
             # To make sure the cache works with padding=True, we pre-padded
             # the audio to multiple of hop_length.
-            hop_length = self.info.get_feature_extractor().hop_length
             mm_data["audio"] = [
                 pad_to_hop_length(audio, hop_length)
                 if isinstance(audio, np.ndarray)
@@ -725,6 +728,14 @@ def pad_to_hop_length(x: np.ndarray, hop_length: int) -> np.ndarray:
             mm_kwargs = dict(
                 **mm_kwargs,
             )
+            # TODO(Isotr0py): Remove this patch after upstream fix PR
+            # released and Transformers version update:
+            # https://github.com/huggingface/transformers/pull/41473
+            if (
+                Version(TRANSFORMERS_VERSION) < Version("4.58.0")
+                and "truncation" not in mm_kwargs
+            ):
+                mm_kwargs["truncation"] = False
 
         hf_inputs = super()._call_hf_processor(
             prompt=prompt,
@@ -738,7 +749,6 @@ def pad_to_hop_length(x: np.ndarray, hop_length: int) -> np.ndarray:
             and "feature_attention_mask" in hf_inputs
             and (audios := mm_data.get("audio", []))
         ):
-            hop_length = self.info.get_feature_extractor().hop_length
             audio_num_frames = []
             for _, audio in enumerate(audios):
                 audio_length = len(audio[0]) if isinstance(audio, tuple) else len(audio)
@@ -747,6 +757,10 @@ def pad_to_hop_length(x: np.ndarray, hop_length: int) -> np.ndarray:
                     if audio_length % hop_length == 0
                     else (audio_length // hop_length - 1)
                 )
+                if mm_kwargs.get("truncation", False):
+                    num_frame = min(
+                        num_frame, feature_extractor.n_samples // hop_length
+                    )
                 audio_num_frames.append(num_frame)
             hf_inputs["feature_attention_mask"] = [
                 torch.ones(num_frame) for num_frame in audio_num_frames