rename method to psd

test/torchaudio_unittest/functional/autograd_impl.py

@@ -256,14 +256,14 @@ def test_bandreject_biquad(self, central_freq, Q):
             (False,),
         ]
     )
-    def test_compute_power_spectral_density_matrix(self, use_mask):
+    def test_psd(self, use_mask):
         torch.random.manual_seed(2434)
         specgram = torch.rand(4, 10, 5, dtype=torch.cfloat)
         if use_mask:
             mask = torch.rand(10, 5)
         else:
             mask = None
-        self.assert_grad(F.compute_power_spectral_density_matrix, (specgram, mask))
+        self.assert_grad(F.psd, (specgram, mask))
 
 
 class AutogradFloat32(TestBaseMixin):

test/torchaudio_unittest/functional/batch_consistency_test.py

@@ -295,7 +295,7 @@ def test_filtfilt(self):
         b = torch.rand(self.batch_size, 3)
         self.assert_batch_consistency(F.filtfilt, inputs=(x, a, b))
 
-    def test_compute_power_spectral_density_matrix(self):
+    def test_psd_matrix(self):
         batch_size = 2
         channel = 3
         sample_rate = 44100
@@ -304,9 +304,9 @@ def test_compute_power_spectral_density_matrix(self):
         waveform = common_utils.get_whitenoise(sample_rate=sample_rate, duration=0.05, n_channels=batch_size * channel)
         specgram = common_utils.get_spectrogram(waveform, n_fft=n_fft, hop_length=100)
         specgram = specgram.view(batch_size, channel, n_fft_bin, specgram.size(-1))
-        self.assert_batch_consistency(F.compute_power_spectral_density_matrix, (specgram,))
+        self.assert_batch_consistency(F.psd, (specgram,))
 
-    def test_compute_power_spectral_density_matrix_with_mask(self):
+    def test_psd_with_mask(self):
         batch_size = 2
         channel = 3
         sample_rate = 44100
@@ -316,4 +316,4 @@ def test_compute_power_spectral_density_matrix_with_mask(self):
         specgram = common_utils.get_spectrogram(waveform, n_fft=n_fft, hop_length=100)
         specgram = specgram.view(batch_size, channel, n_fft_bin, specgram.size(-1))
         mask = torch.rand(batch_size, n_fft_bin, specgram.size(-1))
-        self.assert_batch_consistency(F.compute_power_spectral_density_matrix, (specgram, mask))
+        self.assert_batch_consistency(F.psd, (specgram, mask))

test/torchaudio_unittest/functional/torchscript_consistency_impl.py

@@ -617,9 +617,9 @@ def test_phase_vocoder(self):
         )[..., None]
         self._assert_consistency_complex(F.phase_vocoder, (tensor, rate, phase_advance))
 
-    def test_compute_power_spectral_density_matrix(self):
+    def test_psd(self):
         def func(specgram):
-            return F.compute_power_spectral_density_matrix(specgram)
+            return F.psd(specgram)
 
         batch_size = 2
         channel = 4
@@ -628,9 +628,9 @@ def func(specgram):
         tensor = torch.rand(batch_size, channel, n_fft_bin, frame, dtype=self.complex_dtype)
         self._assert_consistency_complex(func, (tensor,))
 
-    def test_compute_power_spectral_density_matrix_with_mask(self):
+    def test_psd_with_mask(self):
         def func(specgram, mask):
-            return F.compute_power_spectral_density_matrix(specgram, mask)
+            return F.psd(specgram, mask)
 
         batch_size = 2
         channel = 4

torchaudio/functional/__init__.py

@@ -46,7 +46,7 @@
     edit_distance,
     pitch_shift,
     rnnt_loss,
-    compute_power_spectral_density_matrix,
+    psd,
 )
 
 __all__ = [
@@ -95,5 +95,5 @@
     "edit_distance",
     "pitch_shift",
     "rnnt_loss",
-    "compute_power_spectral_density_matrix",
+    "psd",
 ]

torchaudio/functional/functional.py

@@ -37,7 +37,7 @@
     "edit_distance",
     "pitch_shift",
     "rnnt_loss",
-    "compute_power_spectral_density_matrix",
+    "psd",
 ]
 
 
@@ -1634,7 +1634,7 @@ def rnnt_loss(
     return costs
 
 
-def compute_power_spectral_density_matrix(
+def psd(
     specgram: Tensor,
     mask: Optional[Tensor] = None,
     normalize: bool = True,
@@ -1648,7 +1648,7 @@ def compute_power_spectral_density_matrix(
         mask (Tensor or None, optional): Real-valued time-frequency mask
             for normalization. Tensor of dimension `(..., freq, time)`
             (Default: ``None``)
-        normalize (bool, optional): whether to normalize the mask along the time dimension.
+        normalize (bool, optional): whether to normalize the mask along the time dimension. (Default: ``True``)
         eps (float, optional): a value added to the denominator in mask normalization. (Default: ``1e-10``)
 
     Returns: