[Bugfix] Fix Whisper engine crash on audio clips longer than 30s

tests/models/multimodal/processing/test_whisper.py

@@ -0,0 +1,37 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Tests for Whisper's multimodal preprocessing."""
+
+import numpy as np
+import pytest
+
+from vllm.multimodal import MULTIMODAL_REGISTRY
+
+from ...utils import build_model_context
+
+
+@pytest.mark.parametrize("model_id", ["openai/whisper-large-v3-turbo"])
+@pytest.mark.parametrize("audio_duration_s", [5, 30, 35])
+def test_audio_features_fit_encoder_window(
+    model_id: str,
+    audio_duration_s: int,
+) -> None:
+    """The encoder only has positions for one 30 s window, so a longer clip
+    has to be cut to that window like the HF feature extractor does by default.
+    """
+    ctx = build_model_context(model_id, limit_mm_per_prompt={"audio": 1})
+    processor = MULTIMODAL_REGISTRY.create_processor(ctx.model_config)
+    feature_extractor = processor.info.get_feature_extractor()
+
+    sampling_rate = feature_extractor.sampling_rate
+    rng = np.random.RandomState(0)
+    audio = rng.rand(sampling_rate * audio_duration_s).astype(np.float32)
+
+    processed_inputs = processor(
+        "<|startoftranscript|>",
+        mm_items=processor.info.parse_mm_data({"audio": [(audio, sampling_rate)]}),
+        hf_processor_mm_kwargs={},
+    )
+
+    input_features = processed_inputs["mm_kwargs"].get_data()["input_features"]
+    assert input_features.shape[-1] == feature_extractor.nb_max_frames

vllm/model_executor/models/whisper.py

@@ -767,6 +767,7 @@ def _get_hf_mm_inputs(
             hf_kwargs=dict(
                 hf_inputs.hf_kwargs,
                 sampling_rate=feature_extractor.sampling_rate,
+                truncation=True,
             )
         )