Precompute transforms.Resample kernel (#1499)

torchaudio/functional/functional.py

@@ -1299,12 +1299,28 @@ def compute_kaldi_pitch(
 
 
 def _get_sinc_resample_kernel(
-        orig_freq: int,
-        new_freq: int,
+        orig_freq: float,
+        new_freq: float,
+        gcd: int,
         lowpass_filter_width: int,
-        rolloff: float,
-        device: torch.device,
-        dtype: torch.dtype):
+        rolloff: float):
+
+    if not (int(orig_freq) == orig_freq and int(new_freq) == new_freq):
+        warnings.warn(
+            "Non-integer frequencies are being cast to ints and may result in poor resampling quality "
+            "because the underlying algorithm requires an integer ratio between `orig_freq` and `new_freq`. "
+            "Using non-integer valued frequencies will throw an error in the next release. "
+            "To work around this issue, manually convert both frequencies to integer values "
+            "that maintain their resampling rate ratio before passing them into the function "
+            "Example: To downsample a 44100 hz waveform by a factor of 8, use "
+            "`orig_freq=8` and `new_freq=1` instead of `orig_freq=44100` and `new_freq=5512.5` "
+            "For more information or to leave feedback about this change, please refer to "
+            "https://github.com/pytorch/audio/issues/1487."
+        )
+
+    orig_freq = int(orig_freq) // gcd
+    new_freq = int(new_freq) // gcd
+
     assert lowpass_filter_width > 0
     kernels = []
     base_freq = min(orig_freq, new_freq)
@@ -1336,7 +1352,7 @@ 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, device=device, dtype=dtype)
+    idx = torch.arange(-width, width + orig_freq)
 
     for i in range(new_freq):
         t = (-i / new_freq + idx / orig_freq) * base_freq
@@ -1353,6 +1369,34 @@ def _get_sinc_resample_kernel(
     return torch.stack(kernels).view(new_freq, 1, -1).mul_(scale), width
 
 
+def _apply_sinc_resample_kernel(
+        waveform: Tensor,
+        orig_freq: float,
+        new_freq: float,
+        gcd: int,
+        kernel: Tensor,
+        width: int,
+):
+    orig_freq = int(orig_freq) // gcd
+    new_freq = int(new_freq) // gcd
+
+    # pack batch
+    shape = waveform.size()
+    waveform = waveform.view(-1, shape[-1])
+    kernel = kernel.to(device=waveform.device, dtype=waveform.dtype)
+
+    num_wavs, length = waveform.shape
+    waveform = torch.nn.functional.pad(waveform, (width, width + orig_freq))
+    resampled = torch.nn.functional.conv1d(waveform[:, None], kernel, stride=orig_freq)
+    resampled = resampled.transpose(1, 2).reshape(num_wavs, -1)
+    target_length = int(math.ceil(new_freq * length / orig_freq))
+    resampled = resampled[..., :target_length]
+
+    # unpack batch
+    resampled = resampled.view(shape[:-1] + resampled.shape[-1:])
+    return resampled
+
+
 def resample(
         waveform: Tensor,
         orig_freq: float,
@@ -1380,42 +1424,15 @@ def resample(
 
     Returns:
         Tensor: The waveform at the new frequency of dimension (..., time).
+
+    Note: ``transforms.Resample`` precomputes and reuses the resampling kernel, so using it will result in
+    more efficient computation if resampling multiple waveforms with the same resampling parameters.
     """
-    # pack batch
-    shape = waveform.size()
-    waveform = waveform.view(-1, shape[-1])
 
     assert orig_freq > 0.0 and new_freq > 0.0
 
-    if not (int(orig_freq) == orig_freq and int(new_freq) == new_freq):
-        warnings.warn(
-            "Non-integer frequencies are being cast to ints and may result in poor resampling quality "
-            "because the underlying algorithm requires an integer ratio between `orig_freq` and `new_freq`. "
-            "Using non-integer valued frequencies will throw an error in the next release. "
-            "To work around this issue, manually convert both frequencies to integer values "
-            "that maintain their resampling rate ratio before passing them into the function "
-            "Example: To downsample a 44100 hz waveform by a factor of 8, use "
-            "`orig_freq=8` and `new_freq=1` instead of `orig_freq=44100` and `new_freq=5512.5` "
-            "For more information or to leave feedback about this change, please refer to "
-            "https://github.com/pytorch/audio/issues/1487."
-        )
-
-    orig_freq = int(orig_freq)
-    new_freq = int(new_freq)
-    gcd = math.gcd(orig_freq, new_freq)
-    orig_freq = orig_freq // gcd
-    new_freq = new_freq // gcd
-
-    kernel, width = _get_sinc_resample_kernel(orig_freq, new_freq, lowpass_filter_width,
-                                              rolloff, waveform.device, waveform.dtype)
-
-    num_wavs, length = waveform.shape
-    waveform = torch.nn.functional.pad(waveform, (width, width + orig_freq))
-    resampled = torch.nn.functional.conv1d(waveform[:, None], kernel, stride=orig_freq)
-    resampled = resampled.transpose(1, 2).reshape(num_wavs, -1)
-    target_length = int(math.ceil(new_freq * length / orig_freq))
-    resampled = resampled[..., :target_length]
+    gcd = math.gcd(int(orig_freq), int(new_freq))
 
-    # unpack batch
-    resampled = resampled.view(shape[:-1] + resampled.shape[-1:])
+    kernel, width = _get_sinc_resample_kernel(orig_freq, new_freq, gcd, lowpass_filter_width, rolloff)
+    resampled = _apply_sinc_resample_kernel(waveform, orig_freq, new_freq, gcd, kernel, width)
     return resampled

torchaudio/transforms.py

@@ -8,6 +8,10 @@ import torch
 from torch import Tensor
 from torchaudio import functional as F
 
+from .functional.functional import (
+    _get_sinc_resample_kernel,
+    _apply_sinc_resample_kernel,
+)
 
 __all__ = [
     'Spectrogram',
@@ -661,18 +665,23 @@ class Resample(torch.nn.Module):
     """
 
     def __init__(self,
-                 orig_freq: int = 16000,
-                 new_freq: int = 16000,
+                 orig_freq: float = 16000,
+                 new_freq: float = 16000,
                  resampling_method: str = 'sinc_interpolation',
                  lowpass_filter_width: int = 6,
                  rolloff: float = 0.99) -> None:
         super(Resample, self).__init__()
+
         self.orig_freq = orig_freq
         self.new_freq = new_freq
+        self.gcd = math.gcd(int(self.orig_freq), int(self.new_freq))
         self.resampling_method = resampling_method
         self.lowpass_filter_width = lowpass_filter_width
         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)
+
     def forward(self, waveform: Tensor) -> Tensor:
         r"""
         Args:
@@ -682,7 +691,8 @@ class Resample(torch.nn.Module):
             Tensor: Output signal of dimension (..., time).
         """
         if self.resampling_method == 'sinc_interpolation':
-            return F.resample(waveform, self.orig_freq, self.new_freq, self.lowpass_filter_width, self.rolloff)
+            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))