diff --git a/docs/features/multimodal_inputs.md b/docs/features/multimodal_inputs.md
index e9439ffdb..894865208 100644
--- a/docs/features/multimodal_inputs.md
+++ b/docs/features/multimodal_inputs.md
@@ -356,6 +356,44 @@ You can pass a tuple `(array, sampling_rate)` to the `'audio'` field of the mult
 
 Full example: [examples/offline_inference/audio_language.py](../../examples/offline_inference/audio_language.py)
 
+#### Automatic Audio Channel Normalization
+
+vLLM automatically normalizes audio channels for models that require specific audio formats. When loading audio with libraries like `torchaudio`, stereo files return shape `[channels, time]`, but many audio models (particularly Whisper-based models) expect mono audio with shape `[time]`.
+
+**Supported models with automatic mono conversion:**
+
+- **Whisper** and all Whisper-based models
+- **Qwen2-Audio**
+- **Qwen2.5-Omni** / **Qwen3-Omni** (inherits from Qwen2.5-Omni)
+- **Ultravox**
+
+For these models, vLLM automatically:
+
+1. Detects if the model requires mono audio via the feature extractor
+2. Converts multi-channel audio to mono using channel averaging
+3. Handles both `(channels, time)` format (torchaudio) and `(time, channels)` format (soundfile)
+
+**Example with stereo audio:**
+
+```python
+import torchaudio
+from vllm import LLM
+
+# Load stereo audio file - returns (channels, time) shape
+audio, sr = torchaudio.load("stereo_audio.wav")
+print(f"Original shape: {audio.shape}")  # e.g., torch.Size([2, 16000])
+
+# vLLM automatically converts to mono for Whisper-based models
+llm = LLM(model="openai/whisper-large-v3")
+
+outputs = llm.generate({
+    "prompt": "",
+    "multi_modal_data": {"audio": (audio.numpy(), sr)},
+})
+```
+
+No manual conversion is needed - vLLM handles the channel normalization automatically based on the model's requirements.
+
 ### Embedding Inputs
 
 To input pre-computed embeddings belonging to a data type (i.e. image, video, or audio) directly to the language model,
diff --git a/tests/models/multimodal/processing/test_qwen3_omni.py b/tests/models/multimodal/processing/test_qwen3_omni.py
index c80944a52..d66283be4 100644
--- a/tests/models/multimodal/processing/test_qwen3_omni.py
+++ b/tests/models/multimodal/processing/test_qwen3_omni.py
@@ -28,10 +28,9 @@ def test_processor_with_audio_sample_rate(
     """
     Test that vLLM's processor generates expected outputs with audio_sample_rate.
 
-    This validates the reviewer's request that we test the actual processor
-    can handle different audio_sample_rate values and generate audio tokens.
+    This validates that the processor correctly handles audio_sample_rate
+    passed via hf_processor_mm_kwargs and generates audio tokens.
     """
-    # Setup: Build model context and processor
     ctx = build_model_context(
         model_id,
         limit_mm_per_prompt={"audio": 1, "image": 0, "video": 0},
@@ -48,18 +47,17 @@ def test_processor_with_audio_sample_rate(
     prompt = "<|audio_start|><|audio_pad|><|audio_end|>"
     mm_data = {"audio": [(audio_data, audio_sample_rate)]}
 
-    # Execute: Apply processor with audio_sample_rate in mm_kwargs
+    # Apply processor with audio_sample_rate in mm_kwargs
     hf_processor_mm_kwargs: dict[str, Any] = {
         "audio_sample_rate": audio_sample_rate,
     }
     processed_inputs = processor.apply(prompt, mm_data, hf_processor_mm_kwargs)
 
-    # Assert: Verify audio tokens are generated
+    # Verify audio tokens are generated
     hf_processor = processor.info.get_hf_processor(**hf_processor_mm_kwargs)
     audio_token_id = tokenizer.convert_tokens_to_ids(hf_processor.audio_token)
     aud_tok_count = processed_inputs["prompt_token_ids"].count(audio_token_id)
 
-    # Audio should generate at least 1 token
     assert aud_tok_count >= 1, (
         f"Expected at least 1 audio token but got {aud_tok_count}. "
         f"sample_rate: {audio_sample_rate}Hz, duration: {audio_duration_sec}s"
@@ -97,189 +95,10 @@ def test_longer_audio_generates_more_tokens(model_id: str) -> None:
         audio_token_id = tokenizer.convert_tokens_to_ids(hf_proc.audio_token)
         return processed["prompt_token_ids"].count(audio_token_id)
 
-    # Get token counts for different durations
     short_tokens = get_token_count(1.0)
     long_tokens = get_token_count(2.0)
 
-    # Longer audio should produce more tokens
     assert long_tokens > short_tokens, (
         f"Expected longer audio (2s) to have more tokens than shorter (1s). "
         f"Got short={short_tokens}, long={long_tokens}"
     )
-
-
-class TestQwen3OmniAudioSampleRatePreservation:
-    """Test that audio_sample_rate is preserved during kwargs restructuring.
-
-    These tests validate the fix for the audio_sample_rate bug in Qwen3 Omni
-    where the parameter was lost during kwargs restructuring.
-    """
-
-    @staticmethod
-    def _process_kwargs(
-        mm_kwargs: dict[str, Any],
-        tok_kwargs: dict[str, Any],
-        transformers_version: str = "4.57.0",
-    ) -> dict[str, Any]:
-        """
-        Helper method to simulate kwargs processing logic from production code.
-
-        This method simulates the kwargs restructuring that happens in the
-        Qwen3 Omni model when transformers < 4.58.0. By centralizing this
-        logic, we make tests easier to maintain if the production logic changes.
-
-        Args:
-            mm_kwargs: Multimodal kwargs (e.g., audio_sample_rate, truncation)
-            tok_kwargs: Tokenizer kwargs (e.g., truncation)
-            transformers_version: Version string to test against (default: "4.57.0")
-
-        Returns:
-            Processed kwargs dictionary with restructured audio_kwargs and text_kwargs
-        """
-        from packaging.version import Version
-
-        mm_kwargs_copy = dict(mm_kwargs)
-        tok_kwargs_copy = dict(tok_kwargs)
-
-        if Version(transformers_version) < Version("4.58.0"):
-            # Extract audio_sample_rate before restructuring (THE FIX)
-            audio_sample_rate = mm_kwargs_copy.pop("audio_sample_rate", None)
-
-            # Restructure kwargs
-            mm_kwargs_copy["audio_kwargs"] = {
-                "truncation": mm_kwargs_copy.pop("truncation", False)
-            }
-            mm_kwargs_copy["text_kwargs"] = {
-                "truncation": tok_kwargs_copy.pop("truncation", False)
-            }
-
-            # Put audio_sample_rate into audio_kwargs (THE FIX)
-            if audio_sample_rate is not None:
-                mm_kwargs_copy["audio_kwargs"]["audio_sample_rate"] = audio_sample_rate
-
-        return mm_kwargs_copy
-
-    def test_audio_sample_rate_preserved_in_audio_kwargs(self) -> None:
-        """
-        Test that audio_sample_rate is moved from top-level mm_kwargs
-        into audio_kwargs during kwargs restructuring.
-
-        This is the core fix: when transformers < 4.58.0, the code
-        restructures kwargs into audio_kwargs and text_kwargs, and
-        audio_sample_rate must be preserved in audio_kwargs.
-        """
-        # Setup: Create mm_kwargs with audio_sample_rate at top level
-        mm_kwargs: dict[str, Any] = {
-            "audio_sample_rate": 16000,
-            "truncation": True,
-        }
-        tok_kwargs: dict[str, Any] = {
-            "truncation": False,
-        }
-
-        # Execute: Process kwargs using helper method
-        result = self._process_kwargs(mm_kwargs, tok_kwargs)
-
-        # Assert: Verify audio_sample_rate is in audio_kwargs
-        assert "audio_kwargs" in result
-        assert "audio_sample_rate" in result["audio_kwargs"]
-        assert result["audio_kwargs"]["audio_sample_rate"] == 16000
-
-        # Assert: Verify truncation is also in audio_kwargs
-        assert result["audio_kwargs"]["truncation"] is True
-
-        # Assert: Verify text_kwargs is created correctly
-        assert "text_kwargs" in result
-        assert result["text_kwargs"]["truncation"] is False
-
-    def test_audio_sample_rate_absent_when_not_provided(self) -> None:
-        """
-        Test that when audio_sample_rate is not provided in mm_kwargs,
-        the restructured audio_kwargs doesn't contain it.
-        """
-        # Setup: Create mm_kwargs WITHOUT audio_sample_rate
-        mm_kwargs: dict[str, Any] = {
-            "truncation": True,
-        }
-        tok_kwargs: dict[str, Any] = {
-            "truncation": False,
-        }
-
-        # Execute: Process kwargs using helper method
-        result = self._process_kwargs(mm_kwargs, tok_kwargs)
-
-        # Assert: Verify audio_sample_rate is NOT in audio_kwargs
-        assert "audio_kwargs" in result
-        assert "audio_sample_rate" not in result["audio_kwargs"]
-
-        # Assert: Verify truncation is still in audio_kwargs
-        assert result["audio_kwargs"]["truncation"] is True
-
-    @pytest.mark.parametrize("sample_rate", [8000, 16000, 22050, 24000, 44100, 48000])
-    def test_various_audio_sample_rates_preserved(self, sample_rate: int) -> None:
-        """
-        Test that various common audio sample rates are preserved.
-
-        Common sample rates:
-        - 8000: Telephone quality
-        - 16000: Wideband speech (Qwen3 Omni default)
-        - 22050: Low-quality audio
-        - 24000: High-quality speech
-        - 44100: CD quality
-        - 48000: Professional audio
-        """
-        # Setup: Create mm_kwargs with specific sample rate
-        mm_kwargs: dict[str, Any] = {
-            "audio_sample_rate": sample_rate,
-            "truncation": True,
-        }
-        tok_kwargs: dict[str, Any] = {"truncation": False}
-
-        # Execute: Process kwargs using helper method
-        result = self._process_kwargs(mm_kwargs, tok_kwargs)
-
-        # Assert: Verify the specific sample rate is preserved
-        assert result["audio_kwargs"]["audio_sample_rate"] == sample_rate
-
-    def test_kwargs_unchanged_for_newer_transformers_version(self) -> None:
-        """
-        Test that kwargs structure remains unchanged for transformers >= 4.58.0.
-
-        This test ensures that when transformers version is 4.58.0 or higher,
-        the kwargs restructuring is bypassed and audio_sample_rate remains
-        at the top level as originally passed.
-        """
-        from packaging.version import Version
-
-        # Setup: Create mm_kwargs with audio_sample_rate at top level
-        mm_kwargs: dict[str, Any] = {
-            "audio_sample_rate": 16000,
-            "truncation": True,
-        }
-        tok_kwargs: dict[str, Any] = {
-            "truncation": False,
-        }
-
-        # Execute: Simulate with transformers >= 4.58.0
-        mm_kwargs_copy = dict(mm_kwargs)
-        tok_kwargs_copy = dict(tok_kwargs)
-
-        transformers_ver = "4.58.0"  # Version that bypasses restructuring
-        if Version(transformers_ver) < Version("4.58.0"):
-            # This block should NOT execute for >= 4.58.0
-            audio_sample_rate = mm_kwargs_copy.pop("audio_sample_rate", None)
-            mm_kwargs_copy["audio_kwargs"] = {
-                "truncation": mm_kwargs_copy.pop("truncation", False)
-            }
-            mm_kwargs_copy["text_kwargs"] = {
-                "truncation": tok_kwargs_copy.pop("truncation", False)
-            }
-            if audio_sample_rate is not None:
-                mm_kwargs_copy["audio_kwargs"]["audio_sample_rate"] = audio_sample_rate
-
-        # Assert: Verify kwargs structure is unchanged
-        assert "audio_kwargs" not in mm_kwargs_copy
-        assert "text_kwargs" not in mm_kwargs_copy
-        assert mm_kwargs_copy["audio_sample_rate"] == 16000
-        assert mm_kwargs_copy["truncation"] is True
-        assert tok_kwargs_copy["truncation"] is False
diff --git a/tests/multimodal/test_audio.py b/tests/multimodal/test_audio.py
index 189b319e5..46545498c 100644
--- a/tests/multimodal/test_audio.py
+++ b/tests/multimodal/test_audio.py
@@ -7,10 +7,16 @@ from unittest.mock import patch
 
 import numpy as np
 import pytest
+import torch
 
 from vllm.multimodal.audio import (
+    MONO_AUDIO_SPEC,
+    PASSTHROUGH_AUDIO_SPEC,
     AudioMediaIO,
     AudioResampler,
+    AudioSpec,
+    ChannelReduction,
+    normalize_audio,
     resample_audio_librosa,
     resample_audio_scipy,
 )
@@ -137,3 +143,500 @@ def test_audio_media_io_encode_base64(dummy_audio):
         decoded = base64.b64decode(out)
         assert decoded == b"dummy_wav_data"
         mock_write.assert_called_once()
+
+
+# ============================================================
+# Tests for normalize_audio function
+# ============================================================
+
+
+class TestNormalizeAudio:
+    """Tests for normalize_audio function with different specs."""
+
+    def test_passthrough_preserves_audio(self):
+        """Passthrough spec should not modify audio."""
+        stereo = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], dtype=np.float32)
+        result = normalize_audio(stereo, PASSTHROUGH_AUDIO_SPEC)
+        np.testing.assert_array_equal(result, stereo)
+
+    def test_mono_spec_with_numpy_stereo(self):
+        """Mono spec should reduce stereo numpy array to 1D."""
+        stereo = np.array([[1.0, 2.0], [-1.0, 0.0]], dtype=np.float32)
+        result = normalize_audio(stereo, MONO_AUDIO_SPEC)
+        assert result.ndim == 1
+        np.testing.assert_array_almost_equal(result, [0.0, 1.0])
+
+    def test_mono_spec_with_torch_stereo(self):
+        """Mono spec should reduce stereo torch tensor to 1D."""
+        stereo = torch.tensor([[1.0, 2.0], [-1.0, 0.0]])
+        result = normalize_audio(stereo, MONO_AUDIO_SPEC)
+        assert result.ndim == 1
+        torch.testing.assert_close(result, torch.tensor([0.0, 1.0]))
+
+    def test_mono_passthrough_for_1d_numpy(self):
+        """1D numpy array should pass through unchanged with mono spec."""
+        mono = np.array([1.0, 2.0, 3.0], dtype=np.float32)
+        result = normalize_audio(mono, MONO_AUDIO_SPEC)
+        assert result.ndim == 1
+        np.testing.assert_array_equal(result, mono)
+
+    def test_mono_passthrough_for_1d_torch(self):
+        """1D torch tensor should pass through unchanged with mono spec."""
+        mono = torch.tensor([1.0, 2.0, 3.0])
+        result = normalize_audio(mono, MONO_AUDIO_SPEC)
+        assert result.ndim == 1
+        torch.testing.assert_close(result, mono)
+
+    def test_first_channel_reduction(self):
+        """FIRST reduction should take only the first channel."""
+        spec = AudioSpec(target_channels=1, channel_reduction=ChannelReduction.FIRST)
+        stereo = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
+        result = normalize_audio(stereo, spec)
+        np.testing.assert_array_equal(result, [1.0, 2.0])
+
+    def test_max_channel_reduction(self):
+        """MAX reduction should take max across channels."""
+        spec = AudioSpec(target_channels=1, channel_reduction=ChannelReduction.MAX)
+        stereo = np.array([[1.0, 4.0], [3.0, 2.0]], dtype=np.float32)
+        result = normalize_audio(stereo, spec)
+        np.testing.assert_array_equal(result, [3.0, 4.0])
+
+    def test_sum_channel_reduction(self):
+        """SUM reduction should sum across channels."""
+        spec = AudioSpec(target_channels=1, channel_reduction=ChannelReduction.SUM)
+        stereo = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
+        result = normalize_audio(stereo, spec)
+        np.testing.assert_array_equal(result, [4.0, 6.0])
+
+    def test_invalid_3d_array_raises(self):
+        """3D arrays should raise ValueError."""
+        audio_3d = np.random.randn(2, 3, 4).astype(np.float32)
+        with pytest.raises(ValueError, match="Unsupported audio"):
+            normalize_audio(audio_3d, MONO_AUDIO_SPEC)
+
+    def test_channel_expansion_raises(self):
+        """Expanding from mono to stereo should raise ValueError."""
+        mono = np.array([1.0, 2.0, 3.0], dtype=np.float32)
+        spec = AudioSpec(target_channels=2)
+        with pytest.raises(ValueError, match="Cannot expand"):
+            normalize_audio(mono, spec)
+
+    def test_time_channels_format_numpy(self):
+        """Audio in (time, channels) format should be transposed to (channels, time).
+
+        This handles the case where audio loaders like soundfile return
+        (time, channels) format instead of (channels, time) like torchaudio.
+        """
+        # Create audio in (time, channels) format: 1000 samples, 2 channels
+        audio_time_channels = np.array(
+            [[1.0, -1.0]] * 1000,  # 1000 time steps, 2 channels
+            dtype=np.float32,
+        )
+        assert audio_time_channels.shape == (1000, 2)  # (time, channels)
+
+        result = normalize_audio(audio_time_channels, MONO_AUDIO_SPEC)
+
+        # Should be reduced to mono 1D
+        assert result.ndim == 1
+        assert result.shape == (1000,)
+        # Mean of [1.0, -1.0] at each time step should be 0.0
+        np.testing.assert_array_almost_equal(result, np.zeros(1000))
+
+    def test_time_channels_format_torch(self):
+        """Torch tensor in (time, channels) format should be transposed."""
+        # Create audio in (time, channels) format: 1000 samples, 2 channels
+        audio_time_channels = torch.tensor(
+            [[1.0, -1.0]] * 1000,  # 1000 time steps, 2 channels
+        )
+        assert audio_time_channels.shape == (1000, 2)  # (time, channels)
+
+        result = normalize_audio(audio_time_channels, MONO_AUDIO_SPEC)
+
+        # Should be reduced to mono 1D
+        assert result.ndim == 1
+        assert result.shape == (1000,)
+        # Mean of [1.0, -1.0] at each time step should be 0.0
+        torch.testing.assert_close(result, torch.zeros(1000))
+
+    def test_channels_time_format_preserved(self):
+        """Audio already in (channels, time) format should work correctly."""
+        # Create audio in standard (channels, time) format: 2 channels, 1000 samples
+        audio_channels_time = np.array(
+            [[1.0] * 1000, [-1.0] * 1000],  # 2 channels, 1000 time steps
+            dtype=np.float32,
+        )
+        assert audio_channels_time.shape == (2, 1000)  # (channels, time)
+
+        result = normalize_audio(audio_channels_time, MONO_AUDIO_SPEC)
+
+        # Should be reduced to mono 1D
+        assert result.ndim == 1
+        assert result.shape == (1000,)
+        # Mean of [1.0, -1.0] at each time step should be 0.0
+        np.testing.assert_array_almost_equal(result, np.zeros(1000))
+
+    def test_ambiguous_square_audio_numpy(self):
+        """Square audio arrays (N, N) should use shape[0] > shape[1] heuristic.
+
+        For a square array, shape[0] == shape[1], so no transpose happens
+        and we assume (channels, time) format.
+        """
+        # Create square audio: 4 channels, 4 samples
+        audio_square = np.array(
+            [
+                [1.0, 2.0, 3.0, 4.0],
+                [5.0, 6.0, 7.0, 8.0],
+                [9.0, 10.0, 11.0, 12.0],
+                [13.0, 14.0, 15.0, 16.0],
+            ],
+            dtype=np.float32,
+        )
+        assert audio_square.shape == (4, 4)
+
+        result = normalize_audio(audio_square, MONO_AUDIO_SPEC)
+
+        # Should be reduced to mono 1D with mean across channels (axis 0)
+        assert result.ndim == 1
+        assert result.shape == (4,)
+        # Mean across 4 channels: [1+5+9+13, 2+6+10+14, ...] / 4
+        expected = np.array([7.0, 8.0, 9.0, 10.0])
+        np.testing.assert_array_almost_equal(result, expected)
+
+
+# ============================================================
+# Tests for MultiModalDataParser integration with target_channels
+# ============================================================
+
+
+class TestMultiModalDataParserChannelNormalization:
+    """Tests for MultiModalDataParser.target_channels integration.
+
+    These tests verify that the target_channels parameter is properly used
+    in the _parse_audio_data method to normalize audio channels.
+    """
+
+    def test_parser_normalizes_stereo_to_mono(self):
+        """Parser should normalize stereo to mono when target_channels=1."""
+        from vllm.multimodal.parse import MultiModalDataParser
+
+        # Create parser with mono normalization enabled
+        parser = MultiModalDataParser(
+            target_sr=16000,
+            target_channels=1,
+        )
+
+        # Create stereo audio (simulating torchaudio output)
+        stereo_audio = np.array(
+            [[1.0, 1.0, 1.0], [-1.0, -1.0, -1.0]],  # 2 channels, 3 samples
+            dtype=np.float32,
+        )
+
+        # Parse audio data
+        result = parser._parse_audio_data((stereo_audio, 16000))
+
+        # Check that result is mono (1D)
+        audio_item = result.get(0)
+        assert audio_item.ndim == 1, f"Expected 1D mono audio, got {audio_item.ndim}D"
+        assert audio_item.shape == (3,), f"Expected shape (3,), got {audio_item.shape}"
+        # Channel average of [1, 1, 1] and [-1, -1, -1] should be [0, 0, 0]
+        np.testing.assert_array_almost_equal(audio_item, np.zeros(3))
+
+    def test_parser_preserves_stereo_when_target_channels_none(self):
+        """Parser should preserve stereo when target_channels=None."""
+        from vllm.multimodal.parse import MultiModalDataParser
+
+        # Create parser without channel normalization
+        parser = MultiModalDataParser(
+            target_sr=16000,
+            target_channels=None,
+        )
+
+        # Create stereo audio
+        stereo_audio = np.array(
+            [[1.0, 1.0, 1.0], [-1.0, -1.0, -1.0]],
+            dtype=np.float32,
+        )
+
+        # Parse audio data
+        result = parser._parse_audio_data((stereo_audio, 16000))
+
+        # Check that result preserves original shape (after resampling)
+        audio_item = result.get(0)
+        # When target_channels=None, stereo audio should be preserved
+        assert audio_item.ndim == 2, f"Expected 2D stereo audio, got {audio_item.ndim}D"
+
+    def test_parser_mono_passthrough_when_target_channels_1(self):
+        """Parser should pass through mono audio unchanged when target_channels=1."""
+        from vllm.multimodal.parse import MultiModalDataParser
+
+        # Create parser with mono normalization enabled
+        parser = MultiModalDataParser(
+            target_sr=16000,
+            target_channels=1,
+        )
+
+        # Create mono audio (already 1D)
+        mono_audio = np.random.randn(16000).astype(np.float32)
+
+        # Parse audio data
+        result = parser._parse_audio_data((mono_audio, 16000))
+
+        # Check that result is still mono (1D)
+        audio_item = result.get(0)
+        assert audio_item.ndim == 1
+        assert audio_item.shape == (16000,)
+
+    def test_parser_with_target_channels_2(self):
+        """Parser should reduce 6-channel to 2-channel when target_channels=2."""
+        from vllm.multimodal.parse import MultiModalDataParser
+
+        # Create parser with stereo target
+        parser = MultiModalDataParser(
+            target_sr=16000,
+            target_channels=2,
+        )
+
+        # Create 6-channel audio (5.1 surround)
+        surround_audio = np.random.randn(6, 1000).astype(np.float32)
+
+        # Parse audio data
+        result = parser._parse_audio_data((surround_audio, 16000))
+
+        # Check that result is stereo (2 channels)
+        audio_item = result.get(0)
+        assert audio_item.ndim == 2
+        assert audio_item.shape[0] == 2  # 2 channels
+
+
+# ============================================================
+# End-to-End Audio Pipeline Tests
+# ============================================================
+
+
+class TestAudioPipelineE2E:
+    """End-to-end tests for audio normalization in the full pipeline.
+
+    These tests verify the complete flow from raw audio input through
+    the MultiModalDataParser, simulating different audio loader formats.
+    """
+
+    def test_stereo_audio_normalized_to_mono_e2e(self):
+        """Full pipeline: stereo audio (torchaudio format) → mono output."""
+        from vllm.multimodal.parse import MultiModalDataParser
+
+        # Simulate torchaudio output: (channels, time) format
+        # Stereo audio with left channel = 1.0, right channel = -1.0
+        stereo_torchaudio = np.array(
+            [[1.0] * 16000, [-1.0] * 16000],  # 2 channels, 1 second at 16kHz
+            dtype=np.float32,
+        )
+        assert stereo_torchaudio.shape == (2, 16000)
+
+        # Create parser with mono normalization (like Whisper models)
+        parser = MultiModalDataParser(
+            target_sr=16000,
+            target_channels=1,
+        )
+
+        # Process audio through the parser
+        result = parser._parse_audio_data((stereo_torchaudio, 16000))
+        audio_output = result.get(0)
+
+        # Verify output is mono 1D
+        assert audio_output.ndim == 1, f"Expected 1D, got {audio_output.ndim}D"
+        assert audio_output.shape == (16000,)
+
+        # Verify channel averaging: mean of [1.0, -1.0] = 0.0
+        np.testing.assert_array_almost_equal(audio_output, np.zeros(16000), decimal=5)
+
+    def test_soundfile_format_normalized_to_mono_e2e(self):
+        """Full pipeline: soundfile format (time, channels) → mono output."""
+        from vllm.multimodal.parse import MultiModalDataParser
+
+        # Simulate soundfile output: (time, channels) format
+        # 16000 samples, 2 channels
+        stereo_soundfile = np.array(
+            [[0.5, -0.5]] * 16000,  # Each row is [left, right]
+            dtype=np.float32,
+        )
+        assert stereo_soundfile.shape == (16000, 2)
+
+        # Create parser with mono normalization
+        parser = MultiModalDataParser(
+            target_sr=16000,
+            target_channels=1,
+        )
+
+        # Process audio through the parser
+        result = parser._parse_audio_data((stereo_soundfile, 16000))
+        audio_output = result.get(0)
+
+        # Verify output is mono 1D
+        assert audio_output.ndim == 1, f"Expected 1D, got {audio_output.ndim}D"
+        assert audio_output.shape == (16000,)
+
+        # Verify channel averaging: mean of [0.5, -0.5] = 0.0
+        np.testing.assert_array_almost_equal(audio_output, np.zeros(16000), decimal=5)
+
+    def test_librosa_mono_passthrough_e2e(self):
+        """Full pipeline: librosa mono format → preserved as mono."""
+        from vllm.multimodal.parse import MultiModalDataParser
+
+        # Simulate librosa output: already mono (time,) format
+        mono_librosa = np.random.randn(16000).astype(np.float32)
+        assert mono_librosa.shape == (16000,)
+
+        # Create parser with mono normalization
+        parser = MultiModalDataParser(
+            target_sr=16000,
+            target_channels=1,
+        )
+
+        # Process audio through the parser
+        result = parser._parse_audio_data((mono_librosa, 16000))
+        audio_output = result.get(0)
+
+        # Verify output is still mono 1D
+        assert audio_output.ndim == 1
+        assert audio_output.shape == (16000,)
+
+        # Verify audio content is preserved
+        np.testing.assert_array_almost_equal(audio_output, mono_librosa)
+
+    def test_multichannel_5_1_surround_to_mono_e2e(self):
+        """Full pipeline: 5.1 surround (6 channels) → mono output."""
+        from vllm.multimodal.parse import MultiModalDataParser
+
+        # Simulate 5.1 surround audio: 6 channels
+        surround_audio = np.array(
+            [
+                [1.0] * 8000,  # Front Left
+                [2.0] * 8000,  # Front Right
+                [3.0] * 8000,  # Center
+                [4.0] * 8000,  # LFE (subwoofer)
+                [5.0] * 8000,  # Rear Left
+                [6.0] * 8000,  # Rear Right
+            ],
+            dtype=np.float32,
+        )
+        assert surround_audio.shape == (6, 8000)
+
+        # Create parser with mono normalization
+        parser = MultiModalDataParser(
+            target_sr=16000,
+            target_channels=1,
+        )
+
+        # Process audio through the parser
+        result = parser._parse_audio_data((surround_audio, 16000))
+        audio_output = result.get(0)
+
+        # Verify output is mono 1D
+        assert audio_output.ndim == 1
+
+        # Verify channel averaging: mean of [1,2,3,4,5,6] = 3.5
+        expected_value = (1.0 + 2.0 + 3.0 + 4.0 + 5.0 + 6.0) / 6
+        np.testing.assert_array_almost_equal(
+            audio_output, np.full(8000, expected_value), decimal=5
+        )
+
+    def test_torch_tensor_input_e2e(self):
+        """Full pipeline: torch.Tensor stereo input → mono numpy output."""
+        from vllm.multimodal.parse import MultiModalDataParser
+
+        # Simulate torch tensor input (from torchaudio)
+        stereo_torch = torch.tensor(
+            [[1.0] * 8000, [-1.0] * 8000],  # 2 channels
+            dtype=torch.float32,
+        )
+        assert stereo_torch.shape == (2, 8000)
+
+        # Create parser with mono normalization
+        parser = MultiModalDataParser(
+            target_sr=16000,
+            target_channels=1,
+        )
+
+        # Process audio through the parser
+        # Note: Parser expects numpy, so we convert first (simulating real usage)
+        result = parser._parse_audio_data((stereo_torch.numpy(), 16000))
+        audio_output = result.get(0)
+
+        # Verify output is mono 1D numpy array
+        assert audio_output.ndim == 1
+        assert isinstance(audio_output, np.ndarray)
+
+        # Verify channel averaging
+        np.testing.assert_array_almost_equal(audio_output, np.zeros(8000), decimal=5)
+
+    def test_passthrough_preserves_stereo_e2e(self):
+        """Full pipeline: stereo with target_channels=None → stereo preserved."""
+        from vllm.multimodal.parse import MultiModalDataParser
+
+        # Stereo audio
+        stereo_audio = np.array(
+            [[1.0] * 8000, [-1.0] * 8000],
+            dtype=np.float32,
+        )
+
+        # Create parser WITHOUT mono normalization (passthrough)
+        parser = MultiModalDataParser(
+            target_sr=16000,
+            target_channels=None,  # Passthrough - no normalization
+        )
+
+        # Process audio through the parser
+        result = parser._parse_audio_data((stereo_audio, 16000))
+        audio_output = result.get(0)
+
+        # Verify output preserves stereo (2D)
+        assert audio_output.ndim == 2
+        assert audio_output.shape == (2, 8000)
+
+    def test_resampling_with_channel_normalization_e2e(self):
+        """Full pipeline: resample + channel normalize in single pass."""
+        from vllm.multimodal.parse import MultiModalDataParser
+
+        # Stereo audio at 48kHz (common recording rate)
+        stereo_48k = np.array(
+            [[1.0] * 48000, [-1.0] * 48000],  # 1 second at 48kHz
+            dtype=np.float32,
+        )
+
+        # Create parser with both resampling and mono normalization
+        parser = MultiModalDataParser(
+            target_sr=16000,  # Resample to 16kHz
+            target_channels=1,  # Normalize to mono
+        )
+
+        # Process audio through the parser
+        result = parser._parse_audio_data((stereo_48k, 48000))
+        audio_output = result.get(0)
+
+        # Verify output is mono 1D at target sample rate
+        assert audio_output.ndim == 1
+        # After resampling from 48kHz to 16kHz, length should be ~16000
+        assert audio_output.shape[0] == 16000
+
+    def test_very_short_audio_e2e(self):
+        """Full pipeline: very short audio (< 1 frame) handled correctly."""
+        from vllm.multimodal.parse import MultiModalDataParser
+
+        # Very short stereo audio (10 samples)
+        short_stereo = np.array(
+            [[1.0] * 10, [-1.0] * 10],
+            dtype=np.float32,
+        )
+
+        parser = MultiModalDataParser(
+            target_sr=16000,
+            target_channels=1,
+        )
+
+        result = parser._parse_audio_data((short_stereo, 16000))
+        audio_output = result.get(0)
+
+        # Should still produce mono output
+        assert audio_output.ndim == 1
+        assert audio_output.shape == (10,)
+        np.testing.assert_array_almost_equal(audio_output, np.zeros(10))
diff --git a/vllm/model_executor/models/qwen2_5_omni_thinker.py b/vllm/model_executor/models/qwen2_5_omni_thinker.py
index bc9c8b73c..744c21b54 100644
--- a/vllm/model_executor/models/qwen2_5_omni_thinker.py
+++ b/vllm/model_executor/models/qwen2_5_omni_thinker.py
@@ -226,6 +226,10 @@ class Qwen2_5OmniThinkerProcessingInfo(
         assert isinstance(feature_extractor, WhisperFeatureExtractor)
         return feature_extractor
 
+    def get_target_channels(self) -> int:
+        """Return target audio channels for Qwen2.5 Omni models (mono)."""
+        return 1
+
     def get_supported_mm_limits(self) -> Mapping[str, int | None]:
         return {"audio": None, "image": None, "video": None}
 
@@ -310,6 +314,7 @@ class Qwen2_5OmniThinkerMultiModalProcessor(
         return Qwen2_5OmniThinkerMultiModalDataParser(
             spatial_merge_size=self.info.get_hf_config().vision_config.spatial_merge_size,
             target_sr=feature_extractor.sampling_rate,
+            target_channels=self.info.get_target_channels(),
         )
 
     def _call_hf_processor(
diff --git a/vllm/model_executor/models/qwen2_audio.py b/vllm/model_executor/models/qwen2_audio.py
index c97e6873e..bc159b927 100644
--- a/vllm/model_executor/models/qwen2_audio.py
+++ b/vllm/model_executor/models/qwen2_audio.py
@@ -140,6 +140,10 @@ class Qwen2AudioProcessingInfo(BaseProcessingInfo):
         assert isinstance(feature_extractor, WhisperFeatureExtractor)
         return feature_extractor
 
+    def get_target_channels(self) -> int:
+        """Return target audio channels for Qwen2 Audio models (mono)."""
+        return 1
+
     def get_supported_mm_limits(self) -> Mapping[str, int | None]:
         return {"audio": None}
 
@@ -201,7 +205,10 @@ class Qwen2AudioMultiModalDataParser(MultiModalDataParser):
 class Qwen2AudioMultiModalProcessor(BaseMultiModalProcessor[Qwen2AudioProcessingInfo]):
     def _get_data_parser(self) -> MultiModalDataParser:
         feature_extractor = self.info.get_feature_extractor()
-        return Qwen2AudioMultiModalDataParser(target_sr=feature_extractor.sampling_rate)
+        return Qwen2AudioMultiModalDataParser(
+            target_sr=feature_extractor.sampling_rate,
+            target_channels=self.info.get_target_channels(),
+        )
 
     def _call_hf_processor(
         self,
diff --git a/vllm/model_executor/models/ultravox.py b/vllm/model_executor/models/ultravox.py
index 7e1b7c90c..7fb46979b 100644
--- a/vllm/model_executor/models/ultravox.py
+++ b/vllm/model_executor/models/ultravox.py
@@ -133,6 +133,10 @@ class UltravoxProcessingInfo(BaseProcessingInfo):
         assert isinstance(feature_extractor, WhisperFeatureExtractor)
         return feature_extractor
 
+    def get_target_channels(self) -> int:
+        """Return target audio channels for Ultravox models (mono)."""
+        return 1
+
     def get_supported_mm_limits(self) -> Mapping[str, int | None]:
         return {"audio": None}
 
@@ -169,7 +173,10 @@ class UltravoxDummyInputsBuilder(BaseDummyInputsBuilder[UltravoxProcessingInfo])
 class UltravoxMultiModalProcessor(BaseMultiModalProcessor[UltravoxProcessingInfo]):
     def _get_data_parser(self) -> MultiModalDataParser:
         feature_extractor = self.info.get_feature_extractor()
-        return MultiModalDataParser(target_sr=feature_extractor.sampling_rate)
+        return MultiModalDataParser(
+            target_sr=feature_extractor.sampling_rate,
+            target_channels=self.info.get_target_channels(),
+        )
 
     def _call_hf_processor(
         self,
diff --git a/vllm/model_executor/models/whisper.py b/vllm/model_executor/models/whisper.py
index d1dadc3d5..ccd063d93 100644
--- a/vllm/model_executor/models/whisper.py
+++ b/vllm/model_executor/models/whisper.py
@@ -690,6 +690,10 @@ class WhisperProcessingInfo(BaseProcessingInfo):
         assert isinstance(feature_extractor, WhisperFeatureExtractor)
         return feature_extractor
 
+    def get_target_channels(self) -> int:
+        """Return target audio channels for Whisper models (mono)."""
+        return 1
+
     def get_num_audio_tokens(self) -> int:
         return self.get_hf_config().max_source_positions
 
@@ -724,7 +728,10 @@ class WhisperDummyInputsBuilder(BaseDummyInputsBuilder[WhisperProcessingInfo]):
 class WhisperMultiModalProcessor(EncDecMultiModalProcessor[WhisperProcessingInfo]):
     def _get_data_parser(self) -> MultiModalDataParser:
         feature_extractor = self.info.get_feature_extractor()
-        return MultiModalDataParser(target_sr=feature_extractor.sampling_rate)
+        return MultiModalDataParser(
+            target_sr=feature_extractor.sampling_rate,
+            target_channels=self.info.get_target_channels(),
+        )
 
     @property
     def pad_dummy_encoder_prompt(self) -> bool:
diff --git a/vllm/multimodal/audio.py b/vllm/multimodal/audio.py
index 57e7be634..6e339d2ef 100644
--- a/vllm/multimodal/audio.py
+++ b/vllm/multimodal/audio.py
@@ -1,6 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import base64
+from dataclasses import dataclass
+from enum import Enum
 from io import BytesIO
 from pathlib import Path
 from typing import Literal
@@ -25,6 +27,136 @@ try:
 except ImportError:
     soundfile = PlaceholderModule("soundfile")  # type: ignore[assignment]
 
+# ============================================================
+
+
+class ChannelReduction(str, Enum):
+    """Method to reduce multi-channel audio to target channels."""
+
+    MEAN = "mean"  # Average across channels (default, preserves energy balance)
+    FIRST = "first"  # Take first channel only
+    MAX = "max"  # Take max value across channels
+    SUM = "sum"  # Sum across channels
+
+
+@dataclass
+class AudioSpec:
+    """Specification for target audio format.
+
+    This dataclass defines the expected audio format for a model's feature
+    extractor. It is used to normalize audio data before processing.
+
+    Attributes:
+        target_channels: Number of output channels. None means passthrough
+            (no normalization). 1 = mono, 2 = stereo, etc.
+        channel_reduction: Method to reduce channels when input has more
+            channels than target. Only used when reducing channels.
+    """
+
+    target_channels: int | None = 1
+    channel_reduction: ChannelReduction = ChannelReduction.MEAN
+
+    @property
+    def needs_normalization(self) -> bool:
+        """Whether audio normalization is needed."""
+        return self.target_channels is not None
+
+    def __repr__(self) -> str:
+        if self.target_channels is None:
+            return "AudioSpec(passthrough)"
+        return (
+            f"AudioSpec(channels={self.target_channels}, "
+            f"reduction={self.channel_reduction.value})"
+        )
+
+
+# Pre-defined specs for common use cases
+MONO_AUDIO_SPEC = AudioSpec(target_channels=1, channel_reduction=ChannelReduction.MEAN)
+PASSTHROUGH_AUDIO_SPEC = AudioSpec(target_channels=None)
+
+
+def normalize_audio(
+    audio: npt.NDArray[np.floating] | torch.Tensor,
+    spec: AudioSpec,
+) -> npt.NDArray[np.floating] | torch.Tensor:
+    """Normalize audio to the specified format.
+
+    This function handles channel reduction for multi-channel audio,
+    supporting both numpy arrays and torch tensors.
+
+    Args:
+        audio: Input audio data. Can be:
+            - 1D array/tensor: (time,) - already mono
+            - 2D array/tensor: (channels, time) - standard format from torchaudio
+            - 2D array/tensor: (time, channels) - format from soundfile
+              (will be auto-detected and transposed if time > channels)
+        spec: AudioSpec defining the target format.
+
+    Returns:
+        Normalized audio in the same type as input (numpy or torch).
+        For mono output (target_channels=1), returns 1D array/tensor.
+
+    Raises:
+        ValueError: If audio has unsupported dimensions or channel expansion
+            is requested (e.g., mono to stereo).
+    """
+    if not spec.needs_normalization:
+        return audio
+
+    # Handle 1D audio (already mono)
+    if audio.ndim == 1:
+        if spec.target_channels == 1:
+            return audio
+        raise ValueError(f"Cannot expand mono audio to {spec.target_channels} channels")
+
+    # Handle 2D audio
+    if audio.ndim != 2:
+        raise ValueError(f"Unsupported audio shape: {audio.shape}. Expected 1D or 2D.")
+
+    # Auto-detect format: if shape[0] > shape[1], assume (time, channels)
+    # This handles soundfile format where time dimension is typically much larger
+    if audio.shape[0] > audio.shape[1]:
+        # Transpose from (time, channels) to (channels, time)
+        audio = audio.T if isinstance(audio, np.ndarray) else audio.T
+
+    num_channels = audio.shape[0]
+
+    # No reduction needed if already at target
+    if num_channels == spec.target_channels:
+        return audio
+
+    # Cannot expand channels
+    if num_channels < spec.target_channels:
+        raise ValueError(
+            f"Cannot expand {num_channels} channels to {spec.target_channels}"
+        )
+
+    # Reduce channels
+    is_numpy = isinstance(audio, np.ndarray)
+
+    if spec.target_channels == 1:
+        # Reduce to mono
+        if spec.channel_reduction == ChannelReduction.MEAN:
+            result = np.mean(audio, axis=0) if is_numpy else audio.mean(dim=0)
+        elif spec.channel_reduction == ChannelReduction.FIRST:
+            result = audio[0]
+        elif spec.channel_reduction == ChannelReduction.MAX:
+            result = np.max(audio, axis=0) if is_numpy else audio.max(dim=0).values
+        elif spec.channel_reduction == ChannelReduction.SUM:
+            result = np.sum(audio, axis=0) if is_numpy else audio.sum(dim=0)
+        else:
+            raise ValueError(f"Unknown reduction method: {spec.channel_reduction}")
+        return result
+    else:
+        # Reduce to N channels (take first N and apply reduction if needed)
+        # For now, just take first N channels
+        return audio[: spec.target_channels]
+
+
+# ============================================================
+# Audio Resampling
+# ============================================================
+
 
 def resample_audio_librosa(
     audio: npt.NDArray[np.floating],
diff --git a/vllm/multimodal/parse.py b/vllm/multimodal/parse.py
index 64c03f8d4..fdd2f2905 100644
--- a/vllm/multimodal/parse.py
+++ b/vllm/multimodal/parse.py
@@ -22,7 +22,7 @@ from typing_extensions import assert_never
 from vllm.utils.collection_utils import is_list_of
 from vllm.utils.import_utils import LazyLoader
 
-from .audio import AudioResampler
+from .audio import AudioResampler, AudioSpec, normalize_audio
 from .base import MediaWithBytes
 from .inputs import (
     AudioItem,
@@ -456,6 +456,9 @@ class MultiModalDataParser:
     Args:
         target_sr (float, optional): Enables automatic resampling of audio
             items to the model's expected sampling rate.
+        target_channels (int, optional): Target number of audio channels.
+            If provided, normalizes audio to this many channels (e.g., 1 for mono).
+            If None, audio channels are passed through unchanged.
         expected_hidden_size (int, optional): Expected hidden dimension for
             embedding inputs. If provided, validates that user-supplied
             embeddings have the correct hidden size to prevent crashes
@@ -466,6 +469,7 @@ class MultiModalDataParser:
         self,
         *,
         target_sr: float | None = None,
+        target_channels: int | None = None,
         audio_resample_method: Literal["librosa", "scipy"] = "librosa",
         video_needs_metadata: bool = False,
         expected_hidden_size: int | None = None,
@@ -476,6 +480,7 @@ class MultiModalDataParser:
             target_sr=target_sr,
             method=audio_resample_method,
         )
+        self.target_channels = target_channels
         self.video_needs_metadata = video_needs_metadata
         self.expected_hidden_size = expected_hidden_size
 
@@ -565,6 +570,11 @@ class MultiModalDataParser:
             else:
                 new_audio = self.audio_resampler.resample(audio, orig_sr=orig_sr)
 
+            # Apply channel normalization if target_channels is set
+            if self.target_channels is not None:
+                spec = AudioSpec(target_channels=self.target_channels)
+                new_audio = normalize_audio(new_audio, spec)
+
             new_audios.append(new_audio)
 
         return AudioProcessorItems(new_audios)