Repository: huggingface/transformers
Pull request: Fix incorrect batching audio index calculation for Phi-4-Multimodal 
# What does this PR do?

<!--
Congratulations! You've made it this far! You're not quite done yet though.

Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.

Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.

Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
-->

<!-- Remove if not applicable -->

Fixes [#38098](https://github.com/huggingface/transformers/issues/38098) (issue)

- ~~Just notice there is no feature extractor test for Phi-4-MM, let me add one for it, so mark this as draft currently.~~


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker
- vision models: @amyeroberts, @qubvel
- speech models: @eustlb
- graph models: @clefourrier

Library:

- flax: @gante and @Rocketknight1
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- pipelines: @Rocketknight1
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @zach-huggingface and @SunMarc
- chat templates: @Rocketknight1

Integrations:

- deepspeed: HF Trainer/Accelerate: @SunMarc @zach-huggingface
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber

Documentation: @stevhliu

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @Rocketknight1
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1

 -->


Problem statement:
[Bug] Phi-4-multimodal audio processor failed to process multiple audios with close length
### System Info

None

### Who can help?

@zucchini-nlp  @eustlb

### Information

- [x] The official example scripts
- [ ] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

When migrating the Phi-4-MM implementation to HF-format in vLLM, I found the audio processor failed to process multiple audios in similar length, which is probably a bug in audio processor.

Reproducible example:
```python3
import numpy as np
from transformers import AutoProcessor

# Define model path
model_path = "microsoft/Phi-4-multimodal-instruct"

# Load model and processor
processor = AutoProcessor.from_pretrained(model_path, revision="refs/pr/70")

audio = [np.zeros(512), np.zeros(620)]
inputs = processor(text="

", audio=audio, sampling_rate=16000)

display(inputs)
``` 

This will raise error:
```text
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
/tmp/ipykernel_35/3211980912.py in <cell line: 0>()
      9 
     10 audio = [np.zeros(512), np.zeros(620)]
---> 11 inputs = processor(text="

", audio=audio, sampling_rate=16000)
     12 
     13 display(inputs)

/usr/local/lib/python3.11/dist-packages/transformers/models/phi4_multimodal/processing_phi4_multimodal.py in __call__(self, text, images, audio, **kwargs)
    117 
    118         image_inputs = self.image_processor(images, **image_kwargs) if images is not None else {}
--> 119         audio_inputs = self.audio_processor(audio, **audio_kwargs) if audio is not None else {}
    120 
    121         # We pop here for images as we don't need it later

/usr/local/lib/python3.11/dist-packages/transformers/models/phi4_multimodal/feature_extraction_phi4_multimodal.py in __call__(self, raw_speech, sampling_rate, pad_to_multiple_of, padding, max_length, truncation, return_tensors, return_attention_mask, device, **kwargs)
    245         audio_lengths = padded_inputs.audio_lengths
    246 
--> 247         input_features = self._torch_extract_fbank_features(input_features, audio_lengths, device)
    248 
    249         feature_lengths = (audio_lengths - self.win_length) // self.hop_length + 1

/usr/local/lib/python3.11/dist-packages/transformers/models/phi4_multimodal/feature_extraction_phi4_multimodal.py in _torch_extract_fbank_features(self, waveform, audio_lengths, device)
    310                 )
    311                 mask = mask.unsqueeze(-1).expand(-1, -1, self.win_length)
--> 312                 masked_frames = frames[to_mask_batch_idxs, offset_idx:max_idx].masked_fill_(mask, 0)
    313                 frames[to_mask_batch_idxs, offset_idx:max_idx] = masked_frames
    314         # ---

RuntimeError: output with shape [1, 1, 400] doesn't match the broadcast shape [1, 3, 400]
```

If we changed these two audio to `length=51000` and `length=51100` respectively, another error will be raised at the same line:
```python3
import numpy as np
from transformers import AutoProcessor

# Define model path
model_path = "microsoft/Phi-4-multimodal-instruct"

# Load model and processor
processor = AutoProcessor.from_pretrained(model_path, revision="refs/pr/70")

audio = [np.zeros(51000), np.zeros(51100)]
inputs = processor(text="

", audio=audio, sampling_rate=16000)

display(inputs)
```

Error:
```
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
/tmp/ipykernel_35/4075216193.py in <cell line: 0>()
      9 
     10 audio = [np.zeros(51000), np.zeros(51100)]
---> 11 inputs = processor(text="

", audio=audio, sampling_rate=16000)
     12 
     13 display(inputs)

/usr/local/lib/python3.11/dist-packages/transformers/models/phi4_multimodal/processing_phi4_multimodal.py in __call__(self, text, images, audio, **kwargs)
    117 
    118         image_inputs = self.image_processor(images, **image_kwargs) if images is not None else {}
--> 119         audio_inputs = self.audio_processor(audio, **audio_kwargs) if audio is not None else {}
    120 
    121         # We pop here for images as we don't need it later

/usr/local/lib/python3.11/dist-packages/transformers/models/phi4_multimodal/feature_extraction_phi4_multimodal.py in __call__(self, raw_speech, sampling_rate, pad_to_multiple_of, padding, max_length, truncation, return_tensors, return_attention_mask, device, **kwargs)
    245         audio_lengths = padded_inputs.audio_lengths
    246 
--> 247         input_features = self._torch_extract_fbank_features(input_features, audio_lengths, device)
    248 
    249         feature_lengths = (audio_lengths - self.win_length) // self.hop_length + 1

/usr/local/lib/python3.11/dist-packages/transformers/models/phi4_multimodal/feature_extraction_phi4_multimodal.py in _torch_extract_fbank_features(self, waveform, audio_lengths, device)
    310                 )
    311                 mask = mask.unsqueeze(-1).expand(-1, -1, self.win_length)
--> 312                 masked_frames = frames[to_mask_batch_idxs, offset_idx:max_idx].masked_fill_(mask, 0)
    313                 frames[to_mask_batch_idxs, offset_idx:max_idx] = masked_frames
    314         # ---

RuntimeError: The size of tensor a (0) must match the size of tensor b (2) at non-singleton dimension 1
```

### Expected behavior

However, for original processor, these audios can be processed expectedly:
```python3
import numpy as np
from transformers import AutoProcessor

# Define model path
model_path = "microsoft/Phi-4-multimodal-instruct"

# Load model and processor
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)

audio = [(np.zeros(512), 16000), (np.zeros(620), 16000)]
inputs = processor(text="

", audios=audio)

print("audio_embed_sizes:", inputs.audio_embed_sizes)


audio = [(np.zeros(51000), 16000), (np.zeros(51100), 16000)]
inputs = processor(text="

", audios=audio)

print("audio_embed_sizes:", inputs.audio_embed_sizes)
```

And the outputs are reasonable:
```
audio_embed_sizes: tensor([1, 1])
audio_embed_sizes: tensor([40, 40])
```

Hints from issue discussion:
Hi @zucchini-nlp @eustlb,

I’d like to tackle the audio‐processor bug in Phi-4-MM (PR-70) where batching same-length audio raises a tensor-shape error in _torch_extract_fbank_features. I’ve reproduced the issue locally and traced it to mismatched mask/frame dimensions when padding variable-length inputs.

May I submit a PR that (1) unifies input padding logic, (2) adjusts mask application in the feature extractor, and (3) adds regression tests for both list and tuple audio inputs?

Thanks for considering!

Commit message: Merge branch 'main' into fix-phi4mm-audio

Patch to review:
diff --git a/src/transformers/models/phi4_multimodal/feature_extraction_phi4_multimodal.py b/src/transformers/models/phi4_multimodal/feature_extraction_phi4_multimodal.py
index 6f1a19fab65e..1c9aaa32b144 100644
--- a/src/transformers/models/phi4_multimodal/feature_extraction_phi4_multimodal.py
+++ b/src/transformers/models/phi4_multimodal/feature_extraction_phi4_multimodal.py
@@ -300,7 +300,7 @@ def _torch_extract_fbank_features(
             to_mask_batch_idxs = torch.arange(batch_size)[audio_lengths != audio_lengths.max()]
             if to_mask_batch_idxs.numel() > 0:
                 batch_idxs_down = (audio_lengths[to_mask_batch_idxs] - self.win_length) // self.hop_length + 1
-                batch_idxs_up = audio_lengths[to_mask_batch_idxs] // self.hop_length + 1
+                batch_idxs_up = audio_lengths[to_mask_batch_idxs] // self.hop_length - 1
                 offset_idx = batch_idxs_down.min()
                 max_idx = batch_idxs_up.max()
 
diff --git a/tests/models/phi4_multimodal/test_feature_extractor_phi4_multimodal.py b/tests/models/phi4_multimodal/test_feature_extractor_phi4_multimodal.py
new file mode 100644
index 000000000000..0163deec33e0
--- /dev/null
+++ b/tests/models/phi4_multimodal/test_feature_extractor_phi4_multimodal.py
@@ -0,0 +1,288 @@
+# Copyright 2025 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import itertools
+import os
+import random
+import tempfile
+import unittest
+
+import numpy as np
+from datasets import load_dataset
+
+from transformers import Phi4MultimodalFeatureExtractor
+from transformers.testing_utils import check_json_file_has_correct_format, require_torch
+from transformers.utils.import_utils import is_torch_available
+
+from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
+
+
+if is_torch_available():
+    import torch
+
+global_rng = random.Random()
+
+
+def floats_list(shape, scale=1.0, rng=None, name=None):
+    """Creates a random float32 tensor"""
+    if rng is None:
+        rng = global_rng
+
+    values = []
+    for batch_idx in range(shape[0]):
+        values.append([])
+        for _ in range(shape[1]):
+            values[-1].append(rng.random() * scale)
+
+    return values
+
+
+class Phi4MultimodalFeatureExtractionTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        min_seq_length=400,
+        max_seq_length=2000,
+        feature_size=80,
+        hop_length=160,
+        win_length=400,
+        padding_value=0.0,
+        sampling_rate=16_000,
+        return_attention_mask=False,
+        do_normalize=True,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.min_seq_length = min_seq_length
+        self.max_seq_length = max_seq_length
+        self.seq_length_diff = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
+        self.padding_value = padding_value
+        self.sampling_rate = sampling_rate
+        self.return_attention_mask = return_attention_mask
+        self.do_normalize = do_normalize
+        self.feature_size = feature_size
+        self.win_length = win_length
+        self.hop_length = hop_length
+
+    def prepare_feat_extract_dict(self):
+        return {
+            "feature_size": self.feature_size,
+            "hop_length": self.hop_length,
+            "win_length": self.win_length,
+            "padding_value": self.padding_value,
+            "sampling_rate": self.sampling_rate,
+            "return_attention_mask": self.return_attention_mask,
+            "do_normalize": self.do_normalize,
+        }
+
+    def prepare_inputs_for_common(self, equal_length=False, numpify=False):
+        def _flatten(list_of_lists):
+            return list(itertools.chain(*list_of_lists))
+
+        if equal_length:
+            speech_inputs = [floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
+        else:
+            # make sure that inputs increase in size
+            speech_inputs = [
+                floats_list((x, self.feature_size))
+                for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff)
+            ]
+        if numpify:
+            speech_inputs = [np.asarray(x) for x in speech_inputs]
+        return speech_inputs
+
+
+class Phi4MultimodalFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
+    feature_extraction_class = Phi4MultimodalFeatureExtractor
+
+    def setUp(self):
+        self.feat_extract_tester = Phi4MultimodalFeatureExtractionTester(self)
+
+    def test_feat_extract_from_and_save_pretrained(self):
+        feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            saved_file = feat_extract_first.save_pretrained(tmpdirname)[0]
+            check_json_file_has_correct_format(saved_file)
+            feat_extract_second = self.feature_extraction_class.from_pretrained(tmpdirname)
+
+        dict_first = feat_extract_first.to_dict()
+        dict_second = feat_extract_second.to_dict()
+        mel_1 = feat_extract_first.mel_filters
+        mel_2 = feat_extract_second.mel_filters
+        self.assertTrue(np.allclose(mel_1, mel_2))
+        self.assertEqual(dict_first, dict_second)
+
+    def test_feat_extract_to_json_file(self):
+        feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            json_file_path = os.path.join(tmpdirname, "feat_extract.json")
+            feat_extract_first.to_json_file(json_file_path)
+            feat_extract_second = self.feature_extraction_class.from_json_file(json_file_path)
+
+        dict_first = feat_extract_first.to_dict()
+        dict_second = feat_extract_second.to_dict()
+        mel_1 = feat_extract_first.mel_filters
+        mel_2 = feat_extract_second.mel_filters
+        self.assertTrue(np.allclose(mel_1, mel_2))
+        self.assertEqual(dict_first, dict_second)
+
+    def test_feat_extract_from_pretrained_kwargs(self):
+        feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            saved_file = feat_extract_first.save_pretrained(tmpdirname)[0]
+            check_json_file_has_correct_format(saved_file)
+            feat_extract_second = self.feature_extraction_class.from_pretrained(
+                tmpdirname, feature_size=2 * self.feat_extract_dict["feature_size"]
+            )
+
+        mel_1 = feat_extract_first.mel_filters
+        mel_2 = feat_extract_second.mel_filters
+        self.assertTrue(2 * mel_1.shape[1] == mel_2.shape[1])
+
+    def test_call(self):
+        # Tests that all call wrap to encode_plus and batch_encode_plus
+        feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
+        # create three inputs of length 800, 1000, and 1200
+        speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
+        np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs]
+        pt_speech_inputs = [torch.tensor(speech_input) for speech_input in speech_inputs]
+
+        # Test feature size
+        input_features = feature_extractor(np_speech_inputs, return_tensors="np").audio_input_features
+        max_audio_len = (1200 - feature_extractor.win_length) // feature_extractor.hop_length + 1
+        self.assertTrue(input_features.ndim == 3)
+        self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size)
+        self.assertTrue(input_features.shape[-2] == max_audio_len)
+
+        # Test not batched input
+        encoded_sequences_1 = feature_extractor(pt_speech_inputs[0], return_tensors="np").audio_input_features
+        encoded_sequences_2 = feature_extractor(np_speech_inputs[0], return_tensors="np").audio_input_features
+        self.assertTrue(np.allclose(encoded_sequences_1, encoded_sequences_2, atol=1e-3))
+
+        # Test batched
+        encoded_sequences_1 = feature_extractor(pt_speech_inputs, return_tensors="np").audio_input_features
+        encoded_sequences_2 = feature_extractor(np_speech_inputs, return_tensors="np").audio_input_features
+        for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
+            self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
+
+        # Test 2-D numpy arrays are batched.
+        speech_inputs = [floats_list((1, x))[0] for x in (800, 800, 800)]
+        np_speech_inputs = np.asarray(speech_inputs)
+        pt_speech_inputs = torch.tensor(speech_inputs)
+        encoded_sequences_1 = feature_extractor(pt_speech_inputs, return_tensors="np").audio_input_features
+        encoded_sequences_2 = feature_extractor(np_speech_inputs, return_tensors="np").audio_input_features
+        for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
+            self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
+
+    @require_torch
+    def test_double_precision_pad(self):
+        import torch
+
+        feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
+        np_speech_inputs = np.random.rand(100, 32).astype(np.float64)
+        py_speech_inputs = np_speech_inputs.tolist()
+
+        for inputs in [py_speech_inputs, np_speech_inputs]:
+            np_processed = feature_extractor.pad([{"audio_input_features": inputs}], return_tensors="np")
+            self.assertTrue(np_processed.audio_input_features.dtype == np.float32)
+            pt_processed = feature_extractor.pad([{"audio_input_features": inputs}], return_tensors="pt")
+            self.assertTrue(pt_processed.audio_input_features.dtype == torch.float32)
+
+    def _load_datasamples(self, num_samples):
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+        # automatic decoding with librispeech
+        speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
+
+        return [x["array"] for x in speech_samples]
+
+    @require_torch
+    def test_torch_integration(self):
+        # fmt: off
+        EXPECTED_INPUT_FEATURES = torch.tensor(
+            [
+                6.5243,  7.2267,  8.0917,  8.0041,  6.8247,  6.3216,  5.9599,  5.6770,
+                5.7441,  5.6138,  6.6793,  6.8597,  5.5375,  6.5330,  5.4880,  7.3280,
+                9.0736,  9.7665,  9.8773, 10.0828, 10.0518, 10.1736, 10.0145,  9.2545,
+                11.0495, 11.6518, 10.8654, 10.2293,  9.1045,  9.4819,
+            ]
+        )
+        # fmt: on
+
+        input_speech = self._load_datasamples(1)
+        feature_extractor = Phi4MultimodalFeatureExtractor()
+        input_features = feature_extractor(input_speech, return_tensors="pt").audio_input_features
+
+        self.assertEqual(input_features.shape, (1, 584, 80))
+        torch.testing.assert_close(input_features[0, 0, :30], EXPECTED_INPUT_FEATURES, rtol=1e-4, atol=1e-4)
+
+    @unittest.mock.patch(
+        "transformers.models.phi4_multimodal.feature_extraction_phi4_multimodal.is_torch_available", lambda: False
+    )
+    def test_numpy_integration(self):
+        # fmt: off
+        EXPECTED_INPUT_FEATURES = np.array(
+            [
+                6.5242944,  7.226712,   8.091721,   8.004097,   6.824679,   6.3216243,
+                5.959894,   5.676975,   5.744051,   5.61384,    6.6793485,  6.8597484,
+                5.5374746,  6.532976,   5.4879804,  7.3279905,  9.073576,   9.766463,
+                9.877262,  10.082759,  10.051792,  10.173581,  10.0144825,  9.254548,
+                11.049487,  11.651841,  10.865354,  10.229329,   9.104464,   9.481946,
+            ]
+        )
+        # fmt: on
+
+        input_speech = self._load_datasamples(1)
+        feature_extractor = Phi4MultimodalFeatureExtractor()
+        input_features = feature_extractor(input_speech, return_tensors="np").audio_input_features
+        self.assertEqual(input_features.shape, (1, 584, 80))
+        self.assertTrue(np.allclose(input_features[0, 0, :30], EXPECTED_INPUT_FEATURES, atol=1e-4))
+
+    @require_torch
+    def test_torch_integration_batch(self):
+        # fmt: off
+        EXPECTED_INPUT_FEATURES = torch.tensor(
+            [
+                [
+                    6.5243,  7.2267,  8.0917,  8.0041,  6.8247,  6.3216,  5.9599,  5.6770,
+                    5.7441,  5.6138,  6.6793,  6.8597,  5.5375,  6.5330,  5.4880,  7.3280,
+                    9.0736,  9.7665,  9.8773, 10.0828, 10.0518, 10.1736, 10.0145,  9.2545,
+                    11.0495, 11.6518, 10.8654, 10.2293,  9.1045,  9.4819
+                ],
+                [
+                    7.5105,  7.9453,  8.6161,  7.7666,  7.2572,  6.8823,  6.3242,  6.1899,
+                    6.9706,  8.0810,  7.3227,  5.8580,  5.4990,  7.7373,  8.5447,  7.7203,
+                    6.3230,  7.1995,  7.1463,  7.3153,  7.4054,  7.2855,  6.9396,  7.0255,
+                    7.3285,  7.2748,  8.0742,  7.3998,  6.4813,  6.7509
+                ],
+                [
+                    7.7932,  8.1604,  8.7653,  8.2080,  7.2630,  6.4537,  4.8394,  6.3153,
+                    8.0207,  8.3379,  6.0896,  5.7369,  5.8601,  4.7598,  4.8850,  6.2529,
+                    3.9354,  6.1577,  7.9921,  9.6577, 10.1449,  9.1414,  9.3361,  9.0022,
+                    9.2533, 10.0548, 10.4372,  8.8550,  9.1266,  9.9013
+                ]
+            ]
+        )
+        # fmt: on
+
+        input_speech = self._load_datasamples(3)
+        feature_extractor = Phi4MultimodalFeatureExtractor()
+        input_features = feature_extractor(input_speech, return_tensors="pt").audio_input_features
+        self.assertEqual(input_features.shape, (3, 1247, 80))
+        print(input_features[:, 0, :30])
+        torch.testing.assert_close(input_features[:, 0, :30], EXPECTED_INPUT_FEATURES, rtol=1e-4, atol=1e-4)


Code review comments:
diff_hunk: @@ -300,7 +300,7 @@ def _torch_extract_fbank_features(
             to_mask_batch_idxs = torch.arange(batch_size)[audio_lengths != audio_lengths.max()]
             if to_mask_batch_idxs.numel() > 0:
                 batch_idxs_down = (audio_lengths[to_mask_batch_idxs] - self.win_length) // self.hop_length + 1
-                batch_idxs_up = audio_lengths[to_mask_batch_idxs] // self.hop_length + 1
+                batch_idxs_up = audio_lengths[to_mask_batch_idxs] // self.hop_length - 1
path: src/transformers/models/phi4_multimodal/feature_extraction_phi4_multimodal.py
text: @user1:
```suggestion
                batch_idxs_up = (audio_lengths[to_mask_batch_idxs] // self.hop_length) - 1
```