Porting the torchaudio kaldi fbank implementation to audio_utils (#26182)

* add kaldi fbank

* make style

* add herz_to_mel_kaldi tests

* add mel to hertz kaldi test

* integration tests

* correct test and remove comment

* make style

* Apply suggestions from code review
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>

* change parameter name

* Apply suggestions from Arthur review
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update remove_dc_offset description

* fix bug  + make style

* fix error in using np.exp instead of np.power

* make style

---------
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

src/transformers/audio_utils.py

@@ -30,17 +30,19 @@ def hertz_to_mel(freq: Union[float, np.ndarray], mel_scale: str = "htk") -> Unio
         freq (`float` or `np.ndarray`):
             The frequency, or multiple frequencies, in hertz (Hz).
         mel_scale (`str`, *optional*, defaults to `"htk"`):
-            The mel frequency scale to use, `"htk"` or `"slaney"`.
+            The mel frequency scale to use, `"htk"`, `"kaldi"` or `"slaney"`.
 
     Returns:
         `float` or `np.ndarray`: The frequencies on the mel scale.
     """
 
-    if mel_scale not in ["slaney", "htk"]:
-        raise ValueError('mel_scale should be one of "htk" or "slaney".')
+    if mel_scale not in ["slaney", "htk", "kaldi"]:
+        raise ValueError('mel_scale should be one of "htk", "slaney" or "kaldi".')
 
     if mel_scale == "htk":
         return 2595.0 * np.log10(1.0 + (freq / 700.0))
+    elif mel_scale == "kaldi":
+        return 1127.0 * np.log(1.0 + (freq / 700.0))
 
     min_log_hertz = 1000.0
     min_log_mel = 15.0
@@ -64,17 +66,19 @@ def mel_to_hertz(mels: Union[float, np.ndarray], mel_scale: str = "htk") -> Unio
         mels (`float` or `np.ndarray`):
             The frequency, or multiple frequencies, in mels.
         mel_scale (`str`, *optional*, `"htk"`):
-            The mel frequency scale to use, `"htk"` or `"slaney"`.
+            The mel frequency scale to use, `"htk"`, `"kaldi"` or `"slaney"`.
 
     Returns:
         `float` or `np.ndarray`: The frequencies in hertz.
     """
 
-    if mel_scale not in ["slaney", "htk"]:
-        raise ValueError('mel_scale should be one of "htk" or "slaney".')
+    if mel_scale not in ["slaney", "htk", "kaldi"]:
+        raise ValueError('mel_scale should be one of "htk", "slaney" or "kaldi".')
 
     if mel_scale == "htk":
-        return 700.0 * (10.0 ** (mels / 2595.0) - 1.0)
+        return 700.0 * (np.power(10, mels / 2595.0) - 1.0)
+    elif mel_scale == "kaldi":
+        return 700.0 * (np.exp(mels / 1127.0) - 1.0)
 
     min_log_hertz = 1000.0
     min_log_mel = 15.0
@@ -120,6 +124,7 @@ def mel_filter_bank(
     sampling_rate: int,
     norm: Optional[str] = None,
     mel_scale: str = "htk",
+    triangularize_in_mel_space: bool = False,
 ) -> np.ndarray:
     """
     Creates a frequency bin conversion matrix used to obtain a mel spectrogram. This is called a *mel filter bank*, and
@@ -155,7 +160,10 @@ def mel_filter_bank(
         norm (`str`, *optional*):
             If `"slaney"`, divide the triangular mel weights by the width of the mel band (area normalization).
         mel_scale (`str`, *optional*, defaults to `"htk"`):
-            The mel frequency scale to use, `"htk"` or `"slaney"`.
+            The mel frequency scale to use, `"htk"`, `"kaldi"` or `"slaney"`.
+        triangularize_in_mel_space (`bool`, *optional*, defaults to `False`):
+            If this option is enabled, the triangular filter is applied in mel space rather than frequency space. This
+            should be set to `true` in order to get the same results as `torchaudio` when computing mel filters.
 
     Returns:
         `np.ndarray` of shape (`num_frequency_bins`, `num_mel_filters`): Triangular filter bank matrix. This is a
@@ -164,15 +172,21 @@ def mel_filter_bank(
     if norm is not None and norm != "slaney":
         raise ValueError('norm must be one of None or "slaney"')
 
-    # frequencies of FFT bins in Hz
-    fft_freqs = np.linspace(0, sampling_rate // 2, num_frequency_bins)
-
     # center points of the triangular mel filters
     mel_min = hertz_to_mel(min_frequency, mel_scale=mel_scale)
     mel_max = hertz_to_mel(max_frequency, mel_scale=mel_scale)
     mel_freqs = np.linspace(mel_min, mel_max, num_mel_filters + 2)
     filter_freqs = mel_to_hertz(mel_freqs, mel_scale=mel_scale)
 
+    if triangularize_in_mel_space:
+        # frequencies of FFT bins in Hz, but filters triangularized in mel space
+        fft_bin_width = sampling_rate / (num_frequency_bins * 2)
+        fft_freqs = hertz_to_mel(fft_bin_width * np.arange(num_frequency_bins), mel_scale=mel_scale)
+        filter_freqs = mel_freqs
+    else:
+        # frequencies of FFT bins in Hz
+        fft_freqs = np.linspace(0, sampling_rate // 2, num_frequency_bins)
+
     mel_filters = _create_triangular_filter_bank(fft_freqs, filter_freqs)
 
     if norm is not None and norm == "slaney":
@@ -218,6 +232,7 @@ def window_function(
         - `"boxcar"`: a rectangular window
         - `"hamming"`: the Hamming window
         - `"hann"`: the Hann window
+        - `"povey"`: the Povey window
 
     Args:
         window_length (`int`):
@@ -243,6 +258,8 @@ def window_function(
         window = np.hamming(length)
     elif name in ["hann", "hann_window"]:
         window = np.hanning(length)
+    elif name in ["povey"]:
+        window = np.power(np.hanning(length), 0.85)
     else:
         raise ValueError(f"Unknown window function '{name}'")
 
@@ -281,6 +298,7 @@ def spectrogram(
     reference: float = 1.0,
     min_value: float = 1e-10,
     db_range: Optional[float] = None,
+    remove_dc_offset: Optional[bool] = None,
     dtype: np.dtype = np.float32,
 ) -> np.ndarray:
     """
@@ -363,6 +381,9 @@ def spectrogram(
         db_range (`float`, *optional*):
             Sets the maximum dynamic range in decibels. For example, if `db_range = 80`, the difference between the
             peak value and the smallest value will never be more than 80 dB. Must be greater than zero.
+        remove_dc_offset (`bool`, *optional*):
+            Subtract mean from waveform on each frame, applied before pre-emphasis. This should be set to `true` in
+            order to get the same results as `torchaudio.compliance.kaldi.fbank` when computing mel filters.
         dtype (`np.dtype`, *optional*, defaults to `np.float32`):
             Data type of the spectrogram tensor. If `power` is None, this argument is ignored and the dtype will be
             `np.complex64`.
@@ -414,6 +435,9 @@ def spectrogram(
     for frame_idx in range(num_frames):
         buffer[:frame_length] = waveform[timestep : timestep + frame_length]
 
+        if remove_dc_offset:
+            buffer[:frame_length] = buffer[:frame_length] - buffer[:frame_length].mean()
+
         if preemphasis is not None:
             buffer[1:frame_length] -= preemphasis * buffer[: frame_length - 1]
             buffer[0] *= 1 - preemphasis

tests/utils/test_audio_utils.py

@@ -45,6 +45,10 @@ class AudioUtilsFunctionTester(unittest.TestCase):
         expected = np.array([0.9, 1.5, 3.0, 15.0, 15.01453781, 25.08188016])
         self.assertTrue(np.allclose(hertz_to_mel(inputs, "slaney"), expected))
 
+        inputs = np.array([60, 100, 200, 1000, 1001, 2000])
+        expected = np.array([92.6824, 150.4899, 283.2313, 999.9907, 1000.6534, 1521.3674])
+        self.assertTrue(np.allclose(hertz_to_mel(inputs, "kaldi"), expected))
+
         with pytest.raises(ValueError):
             hertz_to_mel(100, mel_scale=None)
 
@@ -63,6 +67,10 @@ class AudioUtilsFunctionTester(unittest.TestCase):
         expected = np.array([60, 100, 200, 1000, 1001, 2000])
         self.assertTrue(np.allclose(mel_to_hertz(inputs, "slaney"), expected))
 
+        inputs = np.array([92.6824, 150.4899, 283.2313, 999.9907, 1000.6534, 1521.3674])
+        expected = np.array([60, 100, 200, 1000, 1001, 2000])
+        self.assertTrue(np.allclose(mel_to_hertz(inputs, "kaldi"), expected))
+
         with pytest.raises(ValueError):
             mel_to_hertz(100, mel_scale=None)
 
@@ -89,6 +97,18 @@ class AudioUtilsFunctionTester(unittest.TestCase):
         )
         self.assertEqual(mel_filters.shape, (513, 13))
 
+        mel_filters = mel_filter_bank(
+            num_frequency_bins=513,
+            num_mel_filters=13,
+            min_frequency=100,
+            max_frequency=4000,
+            sampling_rate=16000,
+            norm="slaney",
+            mel_scale="slaney",
+            triangularize_in_mel_space=True,
+        )
+        self.assertEqual(mel_filters.shape, (513, 13))
+
     def test_mel_filter_bank_htk(self):
         mel_filters = mel_filter_bank(
             num_frequency_bins=16,
@@ -153,6 +173,39 @@ class AudioUtilsFunctionTester(unittest.TestCase):
         # fmt: on
         self.assertTrue(np.allclose(mel_filters, expected))
 
+    def test_mel_filter_bank_kaldi(self):
+        mel_filters = mel_filter_bank(
+            num_frequency_bins=16,
+            num_mel_filters=4,
+            min_frequency=0,
+            max_frequency=2000,
+            sampling_rate=4000,
+            norm=None,
+            mel_scale="kaldi",
+            triangularize_in_mel_space=True,
+        )
+        # fmt: off
+        expected = np.array(
+        [[0.0000, 0.0000, 0.0000, 0.0000],
+        [0.6086, 0.0000, 0.0000, 0.0000],
+        [0.8689, 0.1311, 0.0000, 0.0000],
+        [0.4110, 0.5890, 0.0000, 0.0000],
+        [0.0036, 0.9964, 0.0000, 0.0000],
+        [0.0000, 0.6366, 0.3634, 0.0000],
+        [0.0000, 0.3027, 0.6973, 0.0000],
+        [0.0000, 0.0000, 0.9964, 0.0036],
+        [0.0000, 0.0000, 0.7135, 0.2865],
+        [0.0000, 0.0000, 0.4507, 0.5493],
+        [0.0000, 0.0000, 0.2053, 0.7947],
+        [0.0000, 0.0000, 0.0000, 0.9752],
+        [0.0000, 0.0000, 0.0000, 0.7585],
+        [0.0000, 0.0000, 0.0000, 0.5539],
+        [0.0000, 0.0000, 0.0000, 0.3599],
+        [0.0000, 0.0000, 0.0000, 0.1756]]
+        )
+        # fmt: on
+        self.assertTrue(np.allclose(mel_filters, expected, atol=5e-5))
+
     def test_mel_filter_bank_slaney_norm(self):
         mel_filters = mel_filter_bank(
             num_frequency_bins=16,
@@ -271,6 +324,58 @@ class AudioUtilsFunctionTester(unittest.TestCase):
         self.assertEqual(spec.shape, (257, 732))
         self.assertTrue(np.allclose(spec[:64, 400], expected))
 
+        mel_filters = mel_filter_bank(
+            num_frequency_bins=256,
+            num_mel_filters=400,
+            min_frequency=20,
+            max_frequency=8000,
+            sampling_rate=16000,
+            norm=None,
+            mel_scale="kaldi",
+            triangularize_in_mel_space=True,
+        )
+
+        mel_filters = np.pad(mel_filters, ((0, 1), (0, 0)))
+
+        spec = spectrogram(
+            waveform,
+            window_function(400, "povey", periodic=False),
+            frame_length=400,
+            hop_length=160,
+            fft_length=512,
+            power=2.0,
+            center=False,
+            pad_mode="reflect",
+            onesided=True,
+            preemphasis=0.97,
+            mel_filters=mel_filters,
+            log_mel="log",
+            mel_floor=1.1920928955078125e-07,
+            remove_dc_offset=True,
+        )
+        self.assertEqual(spec.shape, (400, 584))
+
+        # fmt: off
+        expected = np.array([-15.94238515,  -8.20712299,  -8.22704352, -15.94238515,
+       -15.94238515, -15.94238515, -15.94238515, -15.94238515,
+        -6.52463769,  -7.73677889, -15.94238515, -15.94238515,
+       -15.94238515, -15.94238515,  -4.18650018,  -3.37195286,
+       -15.94238515, -15.94238515, -15.94238515, -15.94238515,
+        -4.70190154,  -2.4217066 , -15.94238515, -15.94238515,
+       -15.94238515, -15.94238515,  -5.62755239,  -3.53385194,
+       -15.94238515, -15.94238515, -15.94238515, -15.94238515,
+        -9.43303023,  -8.77480925, -15.94238515, -15.94238515,
+       -15.94238515, -15.94238515,  -4.2951092 ,  -5.51585994,
+       -15.94238515, -15.94238515, -15.94238515,  -4.40151721,
+        -3.95228878, -15.94238515, -15.94238515, -15.94238515,
+        -6.10365415,  -4.59494697, -15.94238515, -15.94238515,
+       -15.94238515,  -8.10727767,  -6.2585298 , -15.94238515,
+       -15.94238515, -15.94238515,  -5.60161702,  -4.47217004,
+       -15.94238515, -15.94238515, -15.94238515,  -5.91641988]
+        )
+        # fmt: on
+        self.assertTrue(np.allclose(spec[:64, 400], expected, atol=1e-5))
+
     def test_spectrogram_center_padding(self):
         waveform = self._load_datasamples(1)[0]