rebase

torchaudio/functional/functional.py

@@ -1305,7 +1305,9 @@ def _get_sinc_resample_kernel(
         lowpass_filter_width: int,
         rolloff: float,
         resampling_method: str,
-        beta: Optional[float]):
+        beta: Optional[float],
+        device: torch.device = torch.device("cpu"),
+        dtype: Optional[torch.dtype] = None):
 
     if not (int(orig_freq) == orig_freq and int(new_freq) == new_freq):
         warnings.warn(
@@ -1360,7 +1362,8 @@ 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, dtype=torch.float64)
+    idx_dtype = dtype if dtype is not None else torch.float64
+    idx = torch.arange(-width, width + orig_freq, device=device, dtype=idx_dtype)
 
     for i in range(new_freq):
         t = (-i / new_freq + idx / orig_freq) * base_freq
@@ -1379,7 +1382,10 @@ def _get_sinc_resample_kernel(
         kernels.append(kernel)
 
     scale = base_freq / orig_freq
-    return torch.stack(kernels).view(new_freq, 1, -1).mul_(scale), width
+    kernels = torch.stack(kernels).view(new_freq, 1, -1).mul_(scale)
+    if dtype is None:
+        kernels = kernels.to(dtype=torch.float32)
+    return kernels, width
 
 
 def _apply_sinc_resample_kernel(
@@ -1396,7 +1402,6 @@ def _apply_sinc_resample_kernel(
     # 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))
@@ -1452,6 +1457,6 @@ 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,
-                                              resampling_method, beta)
+                                              resampling_method, beta, waveform.device, waveform.dtype)
     resampled = _apply_sinc_resample_kernel(waveform, orig_freq, new_freq, gcd, kernel, width)
     return resampled

torchaudio/transforms.py

@@ -664,6 +664,11 @@ class Resample(torch.nn.Module):
         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.
+
+        Note: If resampling on waveforms of higher precision than float32, there may be a small loss of precision
+        because the kernel is cached once as float32. If high precision resampling is important for your application,
+        the functional form will retain higher precision, but run slower because it does not cache the kernel.
+        Alternatively, you could rewrite a transform that caches a higher precision kernel.
     """
 
     def __init__(self,
@@ -682,9 +687,10 @@ def __init__(self,
         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,
-                                                            self.resampling_method, beta)
+        kernel, self.width = _get_sinc_resample_kernel(self.orig_freq, self.new_freq, self.gcd,
+                                                       self.lowpass_filter_width, self.rolloff,
+                                                       self.resampling_method, beta)
+        self.register_buffer('kernel', kernel)
 
     def forward(self, waveform: Tensor) -> Tensor:
         r"""