[phi-4] use mel filters from audio utils (#36966)

* use mel_filter_bank from audio utils

* Apply style fixes

---------

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

src/transformers/models/phi4_multimodal/feature_extraction_phi4_multimodal.py

@@ -20,7 +20,7 @@ from typing import Optional, Union
 
 import numpy as np
 
-from ...audio_utils import AudioInput
+from ...audio_utils import AudioInput, mel_filter_bank
 from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
 from ...image_processing_utils import BatchFeature
 from ...utils import TensorType, is_torch_available, logging
@@ -33,66 +33,6 @@ if is_torch_available():
 logger = logging.get_logger(__name__)
 
 
-# TODO: @eustlb, remove this once #36603 is merged.
-def speechlib_mel(sample_rate, n_fft, n_mels, fmin=None, fmax=None):
-    """Create a Mel filter-bank the same as SpeechLib FbankFC.
-
-    Args:
-        sample_rate (int): Sample rate in Hz. number > 0 [scalar]
-        n_fft (int): FFT size. int > 0 [scalar]
-        n_mel (int): Mel filter size. int > 0 [scalar]
-        fmin (float): lowest frequency (in Hz). If None use 0.0.
-            float >= 0 [scalar]
-        fmax: highest frequency (in Hz). If None use sample_rate / 2.
-            float >= 0 [scalar]
-
-    Returns
-        out (numpy.ndarray): Mel transform matrix
-            [shape=(n_mels, 1 + n_fft/2)]
-    """
-
-    bank_width = int(n_fft // 2 + 1)
-    if fmax is None:
-        fmax = sample_rate / 2
-    if fmin is None:
-        fmin = 0
-    assert fmin >= 0, "fmin cannot be negative"
-    assert fmin < fmax <= sample_rate / 2, "fmax must be between (fmin, samplerate / 2]"
-
-    def mel(f):
-        return 1127.0 * np.log(1.0 + f / 700.0)
-
-    def bin2mel(fft_bin):
-        return 1127.0 * np.log(1.0 + fft_bin * sample_rate / (n_fft * 700.0))
-
-    def f2bin(f):
-        return int((f * n_fft / sample_rate) + 0.5)
-
-    # Spec 1: FFT bin range [f2bin(fmin) + 1, f2bin(fmax) - 1]
-    klo = f2bin(fmin) + 1
-    khi = f2bin(fmax)
-
-    khi = max(khi, klo)
-
-    # Spec 2: SpeechLib uses triangles in Mel space
-    mlo = mel(fmin)
-    mhi = mel(fmax)
-    m_centers = np.linspace(mlo, mhi, n_mels + 2)
-    ms = (mhi - mlo) / (n_mels + 1)
-
-    matrix = np.zeros((n_mels, bank_width), dtype=np.float32)
-    for m in range(0, n_mels):
-        left = m_centers[m]
-        center = m_centers[m + 1]
-        right = m_centers[m + 2]
-        for fft_bin in range(klo, khi):
-            mbin = bin2mel(fft_bin)
-            if left < mbin < right:
-                matrix[m, fft_bin] = 1.0 - abs(center - mbin) / ms
-
-    return matrix
-
-
 class Phi4MultimodalFeatureExtractor(SequenceFeatureExtractor):
     model_input_names = ["audio_input_features", "audio_embed_sizes", "audio_attention_mask"]
 
@@ -123,19 +63,15 @@ class Phi4MultimodalFeatureExtractor(SequenceFeatureExtractor):
         self.audio_downsample_rate = audio_downsample_rate
         self.audio_feat_stride = audio_feat_stride
 
-        # TODO: @eustlb, uncomment and remove speechlib_mel once #36603 is merged.
+        self.mel_filters = mel_filter_bank(
-        # self.mel_filters = mel_filter_bank(
+            num_frequency_bins=self.n_fft // 2 + 1,
-        #     num_frequency_bins=self.n_fft // 2 + 1,
+            num_mel_filters=self.feature_size,
-        #     num_mel_filters=self.feature_size,
+            min_frequency=mel_min_frequency,
-        #     min_frequency=mel_min_frequency,
+            max_frequency=mel_max_frequency,
-        #     max_frequency=mel_max_frequency,
+            sampling_rate=self.sampling_rate,
-        #     sampling_rate=self.sampling_rate,
+            triangularize_in_mel_space=True,
-        #     triangularize_in_mel_space=True,
+            mel_scale="kaldi",
-        #     mel_scale="kaldi",
+        )
-        # )
-        self.mel_filters = speechlib_mel(
-            self.sampling_rate, self.n_fft, self.feature_size, mel_min_frequency, mel_max_frequency
-        ).T
 
     def __call__(
         self,