Add kaiser window support to resampling

test/torchaudio_unittest/compliance_kaldi_test.py

@@ -7,6 +7,7 @@
 
 from torchaudio_unittest import common_utils
 from .compliance import utils as compliance_utils
+from parameterized import parameterized
 
 
 def extract_window(window, wave, f, frame_length, frame_shift, snip_edges):
@@ -182,20 +183,26 @@ def get_output_fn(sound, args):
 
         self._compliance_test_helper(self.test2_filepath, 'resample', 32, 3, get_output_fn, atol=1e-2, rtol=1e-5)
 
-    def test_resample_waveform_upsample_size(self):
-        upsample_sound = kaldi.resample_waveform(self.test1_signal, self.test1_signal_sr, self.test1_signal_sr * 2)
+    @parameterized.expand([("sinc_interpolation"), ("kaiser_window")])
+    def test_resample_waveform_upsample_size(self, resampling_method):
+        upsample_sound = kaldi.resample_waveform(self.test1_signal, self.test1_signal_sr, self.test1_signal_sr * 2,
+                                                 resampling_method=resampling_method)
         self.assertTrue(upsample_sound.size(-1) == self.test1_signal.size(-1) * 2)
 
-    def test_resample_waveform_downsample_size(self):
-        downsample_sound = kaldi.resample_waveform(self.test1_signal, self.test1_signal_sr, self.test1_signal_sr // 2)
+    @parameterized.expand([("sinc_interpolation"), ("kaiser_window")])
+    def test_resample_waveform_downsample_size(self, resampling_method):
+        downsample_sound = kaldi.resample_waveform(self.test1_signal, self.test1_signal_sr, self.test1_signal_sr // 2,
+                                                   resampling_method=resampling_method)
         self.assertTrue(downsample_sound.size(-1) == self.test1_signal.size(-1) // 2)
 
-    def test_resample_waveform_identity_size(self):
-        downsample_sound = kaldi.resample_waveform(self.test1_signal, self.test1_signal_sr, self.test1_signal_sr)
+    @parameterized.expand([("sinc_interpolation"), ("kaiser_window")])
+    def test_resample_waveform_identity_size(self, resampling_method):
+        downsample_sound = kaldi.resample_waveform(self.test1_signal, self.test1_signal_sr, self.test1_signal_sr,
+                                                   resampling_method=resampling_method)
         self.assertTrue(downsample_sound.size(-1) == self.test1_signal.size(-1))
 
     def _test_resample_waveform_accuracy(self, up_scale_factor=None, down_scale_factor=None,
-                                         atol=1e-1, rtol=1e-4):
+                                         resampling_method="sinc_interpolation", atol=1e-1, rtol=1e-4):
         # resample the signal and compare it to the ground truth
         n_to_trim = 20
         sample_rate = 1000
@@ -211,7 +218,8 @@ def _test_resample_waveform_accuracy(self, up_scale_factor=None, down_scale_fact
         original_timestamps = torch.arange(0, duration, 1.0 / sample_rate)
 
         sound = 123 * torch.cos(2 * math.pi * 3 * original_timestamps).unsqueeze(0)
-        estimate = kaldi.resample_waveform(sound, sample_rate, new_sample_rate).squeeze()
+        estimate = kaldi.resample_waveform(sound, sample_rate, new_sample_rate,
+                                           resampling_method=resampling_method).squeeze()
 
         new_timestamps = torch.arange(0, duration, 1.0 / new_sample_rate)[:estimate.size(0)]
         ground_truth = 123 * torch.cos(2 * math.pi * 3 * new_timestamps)
@@ -222,27 +230,32 @@ def _test_resample_waveform_accuracy(self, up_scale_factor=None, down_scale_fact
 
         self.assertEqual(estimate, ground_truth, atol=atol, rtol=rtol)
 
-    def test_resample_waveform_downsample_accuracy(self):
+    @parameterized.expand([("sinc_interpolation"), ("kaiser_window")])
+    def test_resample_waveform_downsample_accuracy(self, resampling_method):
         for i in range(1, 20):
-            self._test_resample_waveform_accuracy(down_scale_factor=i * 2)
+            self._test_resample_waveform_accuracy(down_scale_factor=i * 2, resampling_method=resampling_method)
 
-    def test_resample_waveform_upsample_accuracy(self):
+    @parameterized.expand([("sinc_interpolation"), ("kaiser_window")])
+    def test_resample_waveform_upsample_accuracy(self, resampling_method):
         for i in range(1, 20):
-            self._test_resample_waveform_accuracy(up_scale_factor=1.0 + i / 20.0)
+            self._test_resample_waveform_accuracy(up_scale_factor=1.0 + i / 20.0, resampling_method=resampling_method)
 
-    def test_resample_waveform_multi_channel(self):
+    @parameterized.expand([("sinc_interpolation"), ("kaiser_window")])
+    def test_resample_waveform_multi_channel(self, resampling_method):
         num_channels = 3
 
         multi_sound = self.test1_signal.repeat(num_channels, 1)  # (num_channels, 8000 smp)
 
         for i in range(num_channels):
             multi_sound[i, :] *= (i + 1) * 1.5
 
-        multi_sound_sampled = kaldi.resample_waveform(multi_sound, self.test1_signal_sr, self.test1_signal_sr // 2)
+        multi_sound_sampled = kaldi.resample_waveform(multi_sound, self.test1_signal_sr, self.test1_signal_sr // 2,
+                                                      resampling_method=resampling_method)
 
         # check that sampling is same whether using separately or in a tensor of size (c, n)
         for i in range(num_channels):
             single_channel = self.test1_signal * (i + 1) * 1.5
             single_channel_sampled = kaldi.resample_waveform(single_channel, self.test1_signal_sr,
-                                                             self.test1_signal_sr // 2)
+                                                             self.test1_signal_sr // 2,
+                                                             resampling_method=resampling_method)
             self.assertEqual(multi_sound_sampled[i, :], single_channel_sampled[0], rtol=1e-4, atol=1e-7)

test/torchaudio_unittest/transforms/transforms_test.py

@@ -169,9 +169,11 @@ def test_resample_size(self):
 
         upsample_rate = sample_rate * 2
         downsample_rate = sample_rate // 2
-        invalid_resample = torchaudio.transforms.Resample(sample_rate, upsample_rate, resampling_method='foo')
+        invalid_resampling_method = 'foo'
 
-        self.assertRaises(ValueError, invalid_resample, waveform)
+        with self.assertRaises(ValueError):
+            torchaudio.transforms.Resample(sample_rate, upsample_rate,
+                                           resampling_method=invalid_resampling_method)
 
         upsample_resample = torchaudio.transforms.Resample(
             sample_rate, upsample_rate, resampling_method='sinc_interpolation')

torchaudio/compliance/kaldi.py

@@ -756,7 +756,8 @@ def resample_waveform(waveform: Tensor,
                       orig_freq: float,
                       new_freq: float,
                       lowpass_filter_width: int = 6,
-                      rolloff: float = 0.99) -> Tensor:
+                      rolloff: float = 0.99,
+                      resampling_method: str = "sinc_interpolation") -> Tensor:
     r"""Resamples the waveform at the new frequency.
 
     This is a wrapper around ``torchaudio.functional.resample``.
@@ -773,4 +774,5 @@ def resample_waveform(waveform: Tensor,
     Returns:
         Tensor: The waveform at the new frequency
     """
-    return torchaudio.functional.resample(waveform, orig_freq, new_freq, lowpass_filter_width, rolloff)
+    return torchaudio.functional.resample(waveform, orig_freq, new_freq, lowpass_filter_width,
+                                          rolloff, resampling_method)

torchaudio/functional/functional.py

@@ -1303,7 +1303,9 @@ def _get_sinc_resample_kernel(
         new_freq: float,
         gcd: int,
         lowpass_filter_width: int,
-        rolloff: float):
+        rolloff: float,
+        resampling_method: str,
+        beta: Optional[float]):
 
     if not (int(orig_freq) == orig_freq and int(new_freq) == new_freq):
         warnings.warn(
@@ -1318,9 +1320,15 @@ def _get_sinc_resample_kernel(
             "https://github.com/pytorch/audio/issues/1487."
         )
 
+    if resampling_method not in ['sinc_interpolation', 'kaiser_window']:
+        raise ValueError('Invalid resampling method: {}'.format(resampling_method))
+
     orig_freq = int(orig_freq) // gcd
     new_freq = int(new_freq) // gcd
 
+    if resampling_method == "kaiser_window" and beta is None:
+        beta = 14.769656459379492
+
     assert lowpass_filter_width > 0
     kernels = []
     base_freq = min(orig_freq, new_freq)
@@ -1352,15 +1360,20 @@ def _get_sinc_resample_kernel(
     # they will have a lot of almost zero values to the left or to the right...
     # There is probably a way to evaluate those filters more efficiently, but this is kept for
     # future work.
-    idx = torch.arange(-width, width + orig_freq)
+    idx = torch.arange(-width, width + orig_freq, dtype=torch.float64)
 
     for i in range(new_freq):
         t = (-i / new_freq + idx / orig_freq) * base_freq
         t = t.clamp_(-lowpass_filter_width, lowpass_filter_width)
-        t *= math.pi
-        # we do not use torch.hann_window here as we need to evaluate the window
+
+        # we do not use built in torch windows here as we need to evaluate the window
         # at specific positions, not over a regular grid.
-        window = torch.cos(t / lowpass_filter_width / 2)**2
+        if resampling_method == "sinc_interpolation":
+            window = torch.cos(t * math.pi / lowpass_filter_width / 2)**2
+        elif resampling_method == "kaiser_window":
+            beta = torch.tensor(beta, dtype=float)
+            window = torch.i0(beta * torch.sqrt(1 - (t / lowpass_filter_width) ** 2)) / torch.i0(beta)
+        t *= math.pi
         kernel = torch.where(t == 0, torch.tensor(1.).to(t), torch.sin(t) / t)
         kernel.mul_(window)
         kernels.append(kernel)
@@ -1403,6 +1416,8 @@ def resample(
         new_freq: float,
         lowpass_filter_width: int = 6,
         rolloff: float = 0.99,
+        resampling_method: str = "sinc_interpolation",
+        beta: Optional[float] = None,
 ) -> Tensor:
     r"""Resamples the waveform at the new frequency. This matches Kaldi's OfflineFeatureTpl ResampleWaveform
     which uses a LinearResample (resample a signal at linearly spaced intervals to upsample/downsample
@@ -1421,6 +1436,9 @@ def resample(
             but less efficient. We suggest around 4 to 10 for normal use. (Default: ``6``)
         rolloff (float, optional): The roll-off frequency of the filter, as a fraction of the Nyquist.
             Lower values reduce anti-aliasing, but also reduce some of the highest frequencies. (Default: ``0.99``)
+        resampling_method (str, optional): The resampling method.
+            Options: [``sinc_interpolation``, ``kaiser_window``] (Default: ``'sinc_interpolation'``)
+        beta (float, optional): The shape parameter used for kaiser window.
 
     Returns:
         Tensor: The waveform at the new frequency of dimension (..., time).
@@ -1433,6 +1451,7 @@ def resample(
 
     gcd = math.gcd(int(orig_freq), int(new_freq))
 
-    kernel, width = _get_sinc_resample_kernel(orig_freq, new_freq, gcd, lowpass_filter_width, rolloff)
+    kernel, width = _get_sinc_resample_kernel(orig_freq, new_freq, gcd, lowpass_filter_width, rolloff,
+                                              resampling_method, beta)
     resampled = _apply_sinc_resample_kernel(waveform, orig_freq, new_freq, gcd, kernel, width)
     return resampled

torchaudio/transforms.py

@@ -657,19 +657,22 @@ class Resample(torch.nn.Module):
     Args:
         orig_freq (float, optional): The original frequency of the signal. (Default: ``16000``)
         new_freq (float, optional): The desired frequency. (Default: ``16000``)
-        resampling_method (str, optional): The resampling method. (Default: ``'sinc_interpolation'``)
+        resampling_method (str, optional): The resampling method.
+            Options: [``sinc_interpolation``, ``kaiser_window``] (Default: ``'sinc_interpolation'``)
         lowpass_filter_width (int, optional): Controls the sharpness of the filter, more == sharper
             but less efficient. We suggest around 4 to 10 for normal use. (Default: ``6``)
         rolloff (float, optional): The roll-off frequency of the filter, as a fraction of the Nyquist.
             Lower values reduce anti-aliasing, but also reduce some of the highest frequencies. (Default: ``0.99``)
+        beta (float, optional): The shape parameter used for kaiser window.
     """
 
     def __init__(self,
                  orig_freq: float = 16000,
                  new_freq: float = 16000,
                  resampling_method: str = 'sinc_interpolation',
                  lowpass_filter_width: int = 6,
-                 rolloff: float = 0.99) -> None:
+                 rolloff: float = 0.99,
+                 beta: Optional[float] = None) -> None:
         super(Resample, self).__init__()
 
         self.orig_freq = orig_freq
@@ -680,7 +683,8 @@ def __init__(self,
         self.rolloff = rolloff
 
         self.kernel, self.width = _get_sinc_resample_kernel(self.orig_freq, self.new_freq, self.gcd,
-                                                            self.lowpass_filter_width, self.rolloff)
+                                                            self.lowpass_filter_width, self.rolloff,
+                                                            self.resampling_method, beta)
 
     def forward(self, waveform: Tensor) -> Tensor:
         r"""
@@ -690,11 +694,8 @@ def forward(self, waveform: Tensor) -> Tensor:
         Returns:
             Tensor: Output signal of dimension (..., time).
         """
-        if self.resampling_method == 'sinc_interpolation':
-            return _apply_sinc_resample_kernel(waveform, self.orig_freq, self.new_freq, self.gcd,
-                                               self.kernel, self.width)
-
-        raise ValueError('Invalid resampling method: {}'.format(self.resampling_method))
+        return _apply_sinc_resample_kernel(waveform, self.orig_freq, self.new_freq, self.gcd,
+                                           self.kernel, self.width)
 
 
 class ComplexNorm(torch.nn.Module):