Add F.resample torchscript test (#1516)

test/torchaudio_unittest/functional/torchscript_consistency_impl.py

@@ -591,6 +591,28 @@ class Functional(TempDirMixin, TestBaseMixin):
         tensor = common_utils.get_whitenoise(sample_rate=44100)
         self._assert_consistency(func, tensor)
 
+    def test_resample_sinc(self):
+        def func(tensor):
+            sr1, sr2 = 16000., 8000.
+            return F.resample(tensor, sr1, sr2, resampling_method="sinc_interpolation")
+
+        tensor = common_utils.get_whitenoise(sample_rate=16000)
+        self._assert_consistency(func, tensor)
+
+    def test_resample_kaiser(self):
+        def func(tensor):
+            sr1, sr2 = 16000., 8000.
+            return F.resample(tensor, sr1, sr2, resampling_method="kaiser_window")
+
+        def func_beta(tensor):
+            sr1, sr2 = 16000., 8000.
+            beta = 6.
+            return F.resample(tensor, sr1, sr2, resampling_method="kaiser_window", beta=beta)
+
+        tensor = common_utils.get_whitenoise(sample_rate=16000)
+        self._assert_consistency(func, tensor)
+        self._assert_consistency(func_beta, tensor)
+
     @parameterized.expand([(True, ), (False, )])
     def test_phase_vocoder(self, test_paseudo_complex):
         def func(tensor):

torchaudio/functional/functional.py

@@ -1328,9 +1328,6 @@ def _get_sinc_resample_kernel(
     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)
@@ -1373,9 +1370,12 @@ def _get_sinc_resample_kernel(
         # at specific positions, not over a regular grid.
         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)
+        else:
+            # kaiser_window
+            if beta is None:
+                beta = 14.769656459379492
+            beta_tensor = torch.tensor(float(beta))
+            window = torch.i0(beta_tensor * torch.sqrt(1 - (t / lowpass_filter_width) ** 2)) / torch.i0(beta_tensor)
         t *= math.pi
         kernel = torch.where(t == 0, torch.tensor(1.).to(t), torch.sin(t) / t)
         kernel.mul_(window)