[BC-Breaking] Drop pseudo complex support from phase_vocoder / TimeStretch (#1957)

Following the plan #1337, this commit drops the support for pseudo complex type from `F.phase_vocoder` and `T.TimeStretch`.

Author: mthrok

test/torchaudio_unittest/functional/functional_impl.py

@@ -429,11 +429,8 @@ def test_resample_waveform_downsample_accuracy(self, resampling_method, i):
     def test_resample_waveform_upsample_accuracy(self, resampling_method, i):
         self._test_resample_waveform_accuracy(up_scale_factor=1.0 + i / 20.0, resampling_method=resampling_method)
 
-    @nested_params(
-        [0.5, 1.01, 1.3],
-        [True, False],
-    )
-    def test_phase_vocoder_shape(self, rate, test_pseudo_complex):
+    @nested_params([0.5, 1.01, 1.3])
+    def test_phase_vocoder_shape(self, rate):
         """Verify the output shape of phase vocoder"""
         hop_length = 256
         num_freq = 1025
@@ -443,8 +440,6 @@ def test_phase_vocoder_shape(self, rate, test_pseudo_complex):
         torch.random.manual_seed(42)
         spec = torch.randn(
             batch_size, num_freq, num_frames, dtype=self.complex_dtype, device=self.device)
-        if test_pseudo_complex:
-            spec = torch.view_as_real(spec)
 
         phase_advance = torch.linspace(
             0,
@@ -456,7 +451,7 @@ def test_phase_vocoder_shape(self, rate, test_pseudo_complex):
 
         assert spec.dim() == spec_stretch.dim()
         expected_shape = torch.Size([batch_size, num_freq, int(np.ceil(num_frames / rate))])
-        output_shape = (torch.view_as_complex(spec_stretch) if test_pseudo_complex else spec_stretch).shape
+        output_shape = spec_stretch.shape
         assert output_shape == expected_shape
 
     @parameterized.expand(

test/torchaudio_unittest/functional/librosa_compatibility_test_impl.py

@@ -126,11 +126,8 @@ def test_amplitude_to_DB(self):
 
 @unittest.skipIf(not LIBROSA_AVAILABLE, "Librosa not available")
 class FunctionalComplex(TestBaseMixin):
-    @nested_params(
-        [0.5, 1.01, 1.3],
-        [True, False],
-    )
-    def test_phase_vocoder(self, rate, test_pseudo_complex):
+    @nested_params([0.5, 1.01, 1.3])
+    def test_phase_vocoder(self, rate):
         hop_length = 256
         num_freq = 1025
         num_frames = 400
@@ -147,15 +144,11 @@ def test_phase_vocoder(self, rate, test_pseudo_complex):
             device=self.device,
             dtype=torch.float64)[..., None]
 
-        stretched = F.phase_vocoder(
-            torch.view_as_real(spec) if test_pseudo_complex else spec,
-            rate=rate, phase_advance=phase_advance)
+        stretched = F.phase_vocoder(spec, rate=rate, phase_advance=phase_advance)
 
         expected_stretched = librosa.phase_vocoder(
             spec.cpu().numpy(),
             rate=rate,
             hop_length=hop_length)
 
-        self.assertEqual(
-            torch.view_as_complex(stretched) if test_pseudo_complex else stretched,
-            torch.from_numpy(expected_stretched))
+        self.assertEqual(stretched, torch.from_numpy(expected_stretched))

test/torchaudio_unittest/functional/torchscript_consistency_impl.py

@@ -3,7 +3,6 @@
 
 import torch
 import torchaudio.functional as F
-from parameterized import parameterized
 
 from torchaudio_unittest import common_utils
 from torchaudio_unittest.common_utils import (
@@ -31,14 +30,11 @@ def _assert_consistency(self, func, tensor, shape_only=False):
             output = output.shape
         self.assertEqual(ts_output, output)
 
-    def _assert_consistency_complex(self, func, tensor, test_pseudo_complex=False):
+    def _assert_consistency_complex(self, func, tensor):
         assert tensor.is_complex()
         tensor = tensor.to(device=self.device, dtype=self.complex_dtype)
         ts_func = torch_script(func)
 
-        if test_pseudo_complex:
-            tensor = torch.view_as_real(tensor)
-
         torch.random.manual_seed(40)
         output = func(tensor)
 
@@ -641,25 +637,22 @@ def func_beta(tensor):
         self._assert_consistency(func, tensor)
         self._assert_consistency(func_beta, tensor)
 
-    @parameterized.expand([(True, ), (False, )])
-    def test_phase_vocoder(self, test_paseudo_complex):
+    def test_phase_vocoder(self):
         def func(tensor):
-            is_complex = tensor.is_complex()
-
-            n_freq = tensor.size(-2 if is_complex else -3)
+            n_freq = tensor.size(-2)
             rate = 0.5
             hop_length = 256
             phase_advance = torch.linspace(
                 0,
                 3.14 * hop_length,
                 n_freq,
-                dtype=(torch.real(tensor) if is_complex else tensor).dtype,
+                dtype=torch.real(tensor).dtype,
                 device=tensor.device,
             )[..., None]
             return F.phase_vocoder(tensor, rate, phase_advance)
 
         tensor = torch.view_as_complex(torch.randn(2, 1025, 400, 2))
-        self._assert_consistency_complex(func, tensor, test_paseudo_complex)
+        self._assert_consistency_complex(func, tensor)
 
 
 class FunctionalFloat32Only(TestBaseMixin):

test/torchaudio_unittest/transforms/autograd_test_impl.py

@@ -226,11 +226,8 @@ def test_timestretch_zeros_fail(self):
         spectrogram = get_spectrogram(waveform, n_fft=n_fft, power=None)
         self.assert_grad(transform, [spectrogram])
 
-    @nested_params(
-        [0.7, 0.8, 0.9, 1.0, 1.3],
-        [False, True],
-    )
-    def test_timestretch_non_zero(self, rate, test_pseudo_complex):
+    @nested_params([0.7, 0.8, 0.9, 1.0, 1.3])
+    def test_timestretch_non_zero(self, rate):
         """Verify that ``T.TimeStretch`` does not fail if it's not close to 0
 
         ``T.TimeStrech`` is not differentiable around 0, so this test checks the differentiability
@@ -254,8 +251,6 @@ def test_timestretch_non_zero(self, rate, test_pseudo_complex):
         epsilon = 1e-2
         too_close = spectrogram.abs() < epsilon
         spectrogram[too_close] = epsilon * spectrogram[too_close] / spectrogram[too_close].abs()
-        if test_pseudo_complex:
-            spectrogram = torch.view_as_real(spectrogram)
         self.assert_grad(transform, [spectrogram])
 
     def test_psd(self):

test/torchaudio_unittest/transforms/batch_consistency_test.py

@@ -124,20 +124,16 @@ def test_batch_lfcc(self):
 
         self.assert_batch_consistency(transform, waveform, atol=1e-4, rtol=1e-5)
 
-    @parameterized.expand([(True, ), (False, )])
-    def test_batch_TimeStretch(self, test_pseudo_complex):
+    def test_batch_TimeStretch(self):
         rate = 2
         num_freq = 1025
-        num_frames = 400
         batch = 3
 
-        spec = torch.randn(batch, num_freq, num_frames, dtype=torch.complex64)
-        if test_pseudo_complex:
-            spec = torch.view_as_real(spec)
-
+        tensor = common_utils.get_whitenoise(sample_rate=8000, n_channels=batch)
+        spec = common_utils.get_spectrogram(tensor, n_fft=num_freq)
         transform = T.TimeStretch(
             fixed_rate=rate,
-            n_freq=num_freq,
+            n_freq=num_freq // 2 + 1,
             hop_length=512
         )
 

test/torchaudio_unittest/transforms/torchscript_consistency_impl.py

@@ -24,15 +24,13 @@ def _assert_consistency(self, transform, tensor, *args):
         ts_output = ts_transform(tensor, *args)
         self.assertEqual(ts_output, output)
 
-    def _assert_consistency_complex(self, transform, tensor, test_pseudo_complex=False, *args):
+    def _assert_consistency_complex(self, transform, tensor, *args):
         assert tensor.is_complex()
         tensor = tensor.to(device=self.device, dtype=self.complex_dtype)
         transform = transform.to(device=self.device, dtype=self.dtype)
 
         ts_transform = torch_script(transform)
 
-        if test_pseudo_complex:
-            tensor = torch.view_as_real(tensor)
         output = transform(tensor, *args)
         ts_output = ts_transform(tensor, *args)
         self.assertEqual(ts_output, output)
@@ -120,16 +118,21 @@ def test_SpectralCentroid(self):
         waveform = common_utils.get_whitenoise(sample_rate=sample_rate)
         self._assert_consistency(T.SpectralCentroid(sample_rate=sample_rate), waveform)
 
-    @parameterized.expand([(True, ), (False, )])
-    def test_TimeStretch(self, test_pseudo_complex):
-        n_freq = 400
+    def test_TimeStretch(self):
+        n_fft = 1025
+        n_freq = n_fft // 2 + 1
         hop_length = 512
         fixed_rate = 1.3
-        tensor = torch.view_as_complex(torch.rand((10, 2, n_freq, 10, 2)))
+        tensor = torch.rand((10, 2, n_freq, 10), dtype=torch.cfloat)
+        batch = 10
+        num_channels = 2
+
+        waveform = common_utils.get_whitenoise(sample_rate=8000, n_channels=batch * num_channels)
+        tensor = common_utils.get_spectrogram(waveform, n_fft=n_fft)
+        tensor = tensor.reshape(batch, num_channels, n_freq, -1)
         self._assert_consistency_complex(
             T.TimeStretch(n_freq=n_freq, hop_length=hop_length, fixed_rate=fixed_rate),
             tensor,
-            test_pseudo_complex
         )
 
     def test_PitchShift(self):
@@ -152,7 +155,7 @@ def test_PSD_with_mask(self):
         spectrogram = common_utils.get_spectrogram(tensor, n_fft=400, hop_length=100)
         spectrogram = spectrogram.to(self.device)
         mask = torch.rand(spectrogram.shape[-2:], device=self.device)
-        self._assert_consistency_complex(T.PSD(), spectrogram, False, mask)
+        self._assert_consistency_complex(T.PSD(), spectrogram, mask)
 
 
 class TransformsFloat32Only(TestBaseMixin):
@@ -188,5 +191,5 @@ def test_MVDR(self, solution, online):
         mask_n = torch.rand(spectrogram.shape[-2:], device=self.device)
         self._assert_consistency_complex(
             T.MVDR(solution=solution, online=online),
-            spectrogram, False, mask_s, mask_n
+            spectrogram, mask_s, mask_n
         )

torchaudio/functional/functional.py

@@ -714,8 +714,7 @@ def phase_vocoder(
 
     Args:
         complex_specgrams (Tensor):
-            Either a real tensor of dimension of `(..., freq, num_frame, complex=2)`
-            or a tensor of dimension `(..., freq, num_frame)` with complex dtype.
+            A tensor of dimension `(..., freq, num_frame)` with complex dtype.
         rate (float): Speed-up factor
         phase_advance (Tensor): Expected phase advance in each bin. Dimension of `(freq, 1)`
 
@@ -724,7 +723,7 @@ def phase_vocoder(
             Stretched spectrogram. The resulting tensor is of the same dtype as the input
             spectrogram, but the number of frames is changed to ``ceil(num_frame / rate)``.
 
-    Example - With Tensor of complex dtype
+    Example
         >>> freq, hop_length = 1025, 512
         >>> # (channel, freq, time)
         >>> complex_specgrams = torch.randn(2, freq, 300, dtype=torch.cfloat)
@@ -734,41 +733,10 @@ def phase_vocoder(
         >>> x = phase_vocoder(complex_specgrams, rate, phase_advance)
         >>> x.shape # with 231 == ceil(300 / 1.3)
         torch.Size([2, 1025, 231])
-
-    Example - With Tensor of real dtype and extra dimension for complex field
-        >>> freq, hop_length = 1025, 512
-        >>> # (channel, freq, time, complex=2)
-        >>> complex_specgrams = torch.randn(2, freq, 300, 2)
-        >>> rate = 1.3 # Speed up by 30%
-        >>> phase_advance = torch.linspace(
-        >>>    0, math.pi * hop_length, freq)[..., None]
-        >>> x = phase_vocoder(complex_specgrams, rate, phase_advance)
-        >>> x.shape # with 231 == ceil(300 / 1.3)
-        torch.Size([2, 1025, 231, 2])
     """
     if rate == 1.0:
         return complex_specgrams
 
-    if not complex_specgrams.is_complex():
-        warnings.warn(
-            "The support for pseudo complex type in `torchaudio.functional.phase_vocoder` and "
-            "`torchaudio.transforms.TimeStretch` is now deprecated and will be removed "
-            "from 0.11 release."
-            "Please migrate to native complex type by converting the input tensor with "
-            "`torch.view_as_complex`. "
-            "Please refer to https://github.com/pytorch/audio/issues/1337 "
-            "for more details about torchaudio's plan to migrate to native complex type."
-        )
-        if complex_specgrams.size(-1) != 2:
-            raise ValueError(
-                "complex_specgrams must be either native complex tensors or "
-                "real valued tensors with shape (..., 2)")
-
-    is_complex = complex_specgrams.is_complex()
-
-    if not is_complex:
-        complex_specgrams = torch.view_as_complex(complex_specgrams)
-
     # pack batch
     shape = complex_specgrams.size()
     complex_specgrams = complex_specgrams.reshape([-1] + list(shape[-2:]))
@@ -813,9 +781,6 @@ def phase_vocoder(
 
     # unpack batch
     complex_specgrams_stretch = complex_specgrams_stretch.reshape(shape[:-2] + complex_specgrams_stretch.shape[1:])
-
-    if not is_complex:
-        return torch.view_as_real(complex_specgrams_stretch)
     return complex_specgrams_stretch
 
 

torchaudio/transforms.py

@@ -972,8 +972,7 @@ def forward(self, complex_specgrams: Tensor, overriding_rate: Optional[float] =
         r"""
         Args:
             complex_specgrams (Tensor):
-                Either a real tensor of dimension of `(..., freq, num_frame, complex=2)`
-                or a tensor of dimension `(..., freq, num_frame)` with complex dtype.
+                A tensor of dimension `(..., freq, num_frame)` with complex dtype.
             overriding_rate (float or None, optional): speed up to apply to this batch.
                 If no rate is passed, use ``self.fixed_rate``. (Default: ``None``)