Remove lazy behavior from MelScale (#1636)

Rebases #1571; addresses #1569:

"In 0.9.0 we are deprecating the lazy behavior of MelScale because it can make an invalid 
TorchScript object and it does not align with the design of torchaudio. Now in master 
branch, we can remove the implementation."
Co-authored-by: Pankaj Patil <pankaj.patil2099@hotmail.com>
Co-authored-by: moto <855818+mthrok@users.noreply.github.com>
Co-authored-by: hwangjeff <jeffhwang@fb.com>

test/torchaudio_unittest/transforms/batch_consistency_test.py

@@ -33,7 +33,7 @@ class TestTransforms(common_utils.TorchaudioTestCase):
         self.assertEqual(computed, expected)
 
     def test_batch_MelScale(self):
-        specgram = torch.randn(2, 31, 2786)
+        specgram = torch.randn(2, 201, 256)
 
         # Single then transform then batch
         expected = torchaudio.transforms.MelScale()(specgram).repeat(3, 1, 1, 1)
@@ -41,7 +41,7 @@ class TestTransforms(common_utils.TorchaudioTestCase):
         # Batch then transform
         computed = torchaudio.transforms.MelScale()(specgram.repeat(3, 1, 1, 1))
 
-        # shape = (3, 2, 201, 1394)
+        # shape = (3, 2, 128, 256)
         self.assertEqual(computed, expected)
 
     def test_batch_InverseMelScale(self):

test/torchaudio_unittest/transforms/torchscript_consistency_impl.py

@@ -59,10 +59,6 @@ class Transforms(TempDirMixin, TestBaseMixin):
         spec = torch.rand((6, 201))
         self._assert_consistency(T.AmplitudeToDB(), spec)
 
-    def test_MelScale_invalid(self):
-        with self.assertRaises(ValueError):
-            torch.jit.script(T.MelScale())
-
     def test_MelScale(self):
         spec_f = torch.rand((1, 201, 6))
         self._assert_consistency(T.MelScale(n_stft=201), spec_f)

test/torchaudio_unittest/transforms/transforms_test.py

@@ -55,17 +55,17 @@ class Tester(common_utils.TorchaudioTestCase):
         self.assertEqual(mag_to_db_torch, power_to_db_torch)
 
     def test_melscale_load_save(self):
-        specgram = torch.ones(1, 1000, 100)
+        specgram = torch.ones(1, 201, 100)
         melscale_transform = transforms.MelScale()
         melscale_transform(specgram)
 
-        melscale_transform_copy = transforms.MelScale(n_stft=1000)
+        melscale_transform_copy = transforms.MelScale()
         melscale_transform_copy.load_state_dict(melscale_transform.state_dict())
 
         fb = melscale_transform.fb
         fb_copy = melscale_transform_copy.fb
 
-        self.assertEqual(fb_copy.size(), (1000, 128))
+        self.assertEqual(fb_copy.size(), (201, 128))
         self.assertEqual(fb, fb_copy)
 
     def test_melspectrogram_load_save(self):

test/torchaudio_unittest/transforms/transforms_test_impl.py

-import warnings
-
 import torch
 import torchaudio.transforms as T
 
@@ -63,22 +61,6 @@ class TransformsTestBase(TestBaseMixin):
         assert _get_ratio(relative_diff < 1e-3) > 5e-3
         assert _get_ratio(relative_diff < 1e-5) > 1e-5
 
-    def test_melscale_unset_weight_warning(self):
-        """Issue a warning if MelScale initialized without a weight
-
-        As part of the deprecation of lazy intialization behavior (#1510),
-        issue a warning if `n_stft` is not set.
-        """
-        with warnings.catch_warnings(record=True) as caught_warnings:
-            warnings.simplefilter("always")
-            T.MelScale(n_mels=64, sample_rate=8000)
-        assert len(caught_warnings) == 1
-
-        with warnings.catch_warnings(record=True) as caught_warnings:
-            warnings.simplefilter("always")
-            T.MelScale(n_mels=64, sample_rate=8000, n_stft=201)
-        assert len(caught_warnings) == 0
-
     @nested_params(
         ["sinc_interpolation", "kaiser_window"],
         [16000, 44100],

torchaudio/transforms.py

@@ -244,9 +244,8 @@ class MelScale(torch.nn.Module):
         sample_rate (int, optional): Sample rate of audio signal. (Default: ``16000``)
         f_min (float, optional): Minimum frequency. (Default: ``0.``)
         f_max (float or None, optional): Maximum frequency. (Default: ``sample_rate // 2``)
-        n_stft (int, optional): Number of bins in STFT. Calculated from first input
-            if None is given.  See ``n_fft`` in :class:`Spectrogram`. (Default: ``None``)
-        norm (Optional[str]): If 'slaney', divide the triangular mel weights by the width of the mel band
+        n_stft (int, optional): Number of bins in STFT. See ``n_fft`` in :class:`Spectrogram`. (Default: ``201``)
+        norm (str or None, optional): If 'slaney', divide the triangular mel weights by the width of the mel band
         (area normalization). (Default: ``None``)
         mel_scale (str, optional): Scale to use: ``htk`` or ``slaney``. (Default: ``htk``)
     """
@@ -257,7 +256,7 @@ class MelScale(torch.nn.Module):
                  sample_rate: int = 16000,
                  f_min: float = 0.,
                  f_max: Optional[float] = None,
-                 n_stft: Optional[int] = None,
+                 n_stft: int = 201,
                  norm: Optional[str] = None,
                  mel_scale: str = "htk") -> None:
         super(MelScale, self).__init__()
@@ -269,35 +268,11 @@ class MelScale(torch.nn.Module):
         self.mel_scale = mel_scale
 
         assert f_min <= self.f_max, 'Require f_min: {} < f_max: {}'.format(f_min, self.f_max)
-
-        if n_stft is None or n_stft == 0:
-            warnings.warn(
-                'Initialization of torchaudio.transforms.MelScale with an unset weight '
-                '`n_stft=None` is deprecated and will be removed in release 0.10. '
-                'Please set a proper `n_stft` value. Typically this is `n_fft // 2 + 1`. '
-                'Refer to https://github.com/pytorch/audio/issues/1510 '
-                'for more details.'
-            )
-
-        fb = torch.empty(0) if n_stft is None else F.create_fb_matrix(
+        fb = F.create_fb_matrix(
             n_stft, self.f_min, self.f_max, self.n_mels, self.sample_rate, self.norm,
             self.mel_scale)
         self.register_buffer('fb', fb)
 
-    def __prepare_scriptable__(self):
-        r"""If `self.fb` is empty, the `forward` method will try to resize the parameter,
-        which does not work once the transform is scripted. However, this error does not happen
-        until the transform is executed. This is inconvenient especially if the resulting
-        TorchScript object is executed in other environments. Therefore, we check the
-        validity of `self.fb` here and fail if the resulting TS does not work.
-
-        Returns:
-            MelScale: self
-        """
-        if self.fb.numel() == 0:
-            raise ValueError("n_stft must be provided at construction")
-        return self
-
     def forward(self, specgram: Tensor) -> Tensor:
         r"""
         Args:
@@ -311,14 +286,6 @@ class MelScale(torch.nn.Module):
         shape = specgram.size()
         specgram = specgram.reshape(-1, shape[-2], shape[-1])
 
-        if self.fb.numel() == 0:
-            tmp_fb = F.create_fb_matrix(specgram.size(1), self.f_min, self.f_max,
-                                        self.n_mels, self.sample_rate, self.norm,
-                                        self.mel_scale)
-            # Attributes cannot be reassigned outside __init__ so workaround
-            self.fb.resize_(tmp_fb.size())
-            self.fb.copy_(tmp_fb)
-
         # (channel, frequency, time).transpose(...) dot (frequency, n_mels)
         # -> (channel, time, n_mels).transpose(...)
         mel_specgram = torch.matmul(specgram.transpose(1, 2), self.fb).transpose(1, 2)