diff --git a/vllm/model_executor/models/qwen2_audio.py b/vllm/model_executor/models/qwen2_audio.py
index 1cd70a5bf667e..07e29b71c2ed4 100644
--- a/vllm/model_executor/models/qwen2_audio.py
+++ b/vllm/model_executor/models/qwen2_audio.py
@@ -26,8 +26,10 @@
 import numpy as np
 import torch
 import torch.nn as nn
-from transformers import BatchFeature, Qwen2AudioConfig, Qwen2AudioProcessor
-from transformers.models.qwen2_audio import Qwen2AudioEncoder
+from transformers import BatchFeature, ProcessorMixin
+from transformers.models.qwen2_audio import (Qwen2AudioConfig,
+                                             Qwen2AudioEncoder,
+                                             Qwen2AudioProcessor)
 from transformers.models.whisper import WhisperFeatureExtractor
 
 from vllm.attention import AttentionMetadata
@@ -102,6 +104,35 @@ def _get_processor_data(
 
         return super()._get_processor_data(mm_items)
 
+    def _call_hf_processor(
+        self,
+        hf_processor: ProcessorMixin,
+        prompt: str,
+        processor_data: Mapping[str, object],
+        mm_processor_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        processor_data = dict(processor_data)
+        audios = processor_data.pop("audios", [])
+
+        if audios:
+            processor_data["audios"] = audios
+
+            feature_extractor = self._get_feature_extractor()
+            mm_processor_kwargs = dict(
+                **mm_processor_kwargs,
+                sampling_rate=feature_extractor.sampling_rate,
+            )
+        else:
+            # NOTE: WhisperFeatureExtractor cannot handle empty list of audios
+            pass
+
+        return super()._call_hf_processor(
+            hf_processor,
+            prompt=prompt,
+            processor_data=processor_data,
+            mm_processor_kwargs=mm_processor_kwargs,
+        )
+
     def _get_prompt_replacements(
         self,
         mm_items: MultiModalDataItems,
@@ -111,8 +142,12 @@ def _get_prompt_replacements(
         hf_config = self.ctx.get_hf_config(Qwen2AudioConfig)
         placeholder = hf_config.audio_token_index
 
-        _, audio_output_lengths = _get_feat_extract_output_lengths(
-            hf_inputs.feature_attention_mask.sum(-1))
+        feature_attention_mask = hf_inputs.get("feature_attention_mask")
+        if feature_attention_mask is None:
+            audio_output_lengths = []
+        else:
+            _, audio_output_lengths = _get_feat_extract_output_lengths(
+                feature_attention_mask.sum(-1))
 
         def get_replacement_qwen2_audio(item_idx: int):
             return [placeholder] * audio_output_lengths[item_idx]
diff --git a/vllm/model_executor/models/ultravox.py b/vllm/model_executor/models/ultravox.py
index 8a3e56ec32a8a..c60b208c3d27d 100644
--- a/vllm/model_executor/models/ultravox.py
+++ b/vllm/model_executor/models/ultravox.py
@@ -93,7 +93,10 @@ def _call_hf_processor(
         processor_data: Mapping[str, object],
         mm_processor_kwargs: Mapping[str, object],
     ) -> BatchFeature:
-        if "audios" not in processor_data:
+        processor_data = dict(processor_data)
+        audios = processor_data.pop("audios", [])
+
+        if not audios:
             return super()._call_hf_processor(
                 hf_processor,
                 prompt=prompt,
@@ -101,10 +104,13 @@ def _call_hf_processor(
                 mm_processor_kwargs=mm_processor_kwargs,
             )
 
-        shared_processor_data = dict(processor_data)
+        feature_extractor = self._get_feature_extractor()
+        mm_processor_kwargs = dict(
+            **mm_processor_kwargs,
+            sampling_rate=feature_extractor.sampling_rate,
+        )
 
         # Already resampled by _get_processor_data
-        audios = shared_processor_data.pop("audios")
         assert is_list_of(audios, np.ndarray)
 
         # Ultravox processor doesn't support multiple inputs,
@@ -113,7 +119,7 @@ def _call_hf_processor(
         shared_outputs = {}
         for audio in audios:
             # NOTE: Ultravox processor accepts "audio" instead of "audios"
-            item_processor_data = dict(**shared_processor_data, audio=audio)
+            item_processor_data = dict(**processor_data, audio=audio)
 
             item_outputs = super()._call_hf_processor(
                 hf_processor,
diff --git a/vllm/multimodal/processing.py b/vllm/multimodal/processing.py
index 0abbdcd70db88..741a7e7461795 100644
--- a/vllm/multimodal/processing.py
+++ b/vllm/multimodal/processing.py
@@ -661,7 +661,8 @@ def _get_processor_data(
                 if isinstance(v, torch.Tensor) and v.ndim == 3:
                     # Pass through embedding inputs (single)
                     passthrough_data[f"{k}_embeds"] = [v]
-                elif is_list_of(v, torch.Tensor) and v[0].ndim == 2:
+                elif (is_list_of(v, torch.Tensor) and len(v) > 0
+                      and v[0].ndim == 2):
                     # Pass through embedding inputs (multi)
                     passthrough_data[f"{k}_embeds"] = v
                 else: