Griffin-Lim Transformation Implementation

docs/source/transforms.rst

@@ -16,6 +16,13 @@ Transforms are common audio transforms. They can be chained together using :clas
 
   .. automethod:: forward
 
+:hidden:`GriffinLim`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: GriffinLim 
+
+  .. automethod:: forward
+
 :hidden:`AmplitudeToDB`
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

test/test_functional.py

@@ -52,6 +52,47 @@ def test_torchscript_spectrogram(self):
             F.spectrogram, tensor, pad, window, n_fft, hop, ws, power, normalize
         )
 
+    def test_torchscript_griffinlim(self):
+        tensor = torch.rand((1, 201, 6))
+        n_fft = 400
+        ws = 400
+        hop = 200
+        window = torch.hann_window(ws)
+        power = 2
+        normalize = False
+        momentum = 0.99
+        n_iter = 32
+        length = 1000
+        init = 0
+
+        _test_torchscript_functional(
+            F.griffinlim, tensor, window, n_fft, hop, ws, power, normalize, n_iter, momentum, length, 0
+        )
+
+    @unittest.skipIf(not IMPORT_LIBROSA, 'Librosa not available')
+    def test_griffinlim(self):
+        tensor = torch.rand((1, 1000))
+
+        n_fft = 400
+        ws = 400
+        hop = 100
+        window = torch.hann_window(ws)
+        normalize = False
+        momentum = 0.99
+        n_iter = 8
+        length = 1000
+        rand_init = False
+        init = 'random' if rand_init else None
+
+        specgram = F.spectrogram(tensor, 0, window, n_fft, hop, ws, 2, normalize).sqrt()
+        ta_out = F.griffinlim(specgram, window, n_fft, hop, ws, 1, normalize,
+                              n_iter, momentum, length, rand_init)
+        lr_out = librosa.griffinlim(specgram.squeeze(0).numpy(), n_iter=n_iter, hop_length=hop,
+                                    momentum=momentum, init=init, length=length)
+        lr_out = torch.from_numpy(lr_out).unsqueeze(0)
+
+        self.assertTrue(torch.allclose(ta_out, lr_out, atol=5e-5))
+
     def _test_compute_deltas(self, specgram, expected, win_length=3, atol=1e-6, rtol=1e-8):
         computed = F.compute_deltas(specgram, win_length=win_length)
         self.assertTrue(computed.shape == expected.shape, (computed.shape, expected.shape))

test/test_transforms.py

@@ -68,6 +68,10 @@ def test_scriptmodule_Spectrogram(self):
         tensor = torch.rand((1, 1000))
         _test_script_module(transforms.Spectrogram, tensor)
 
+    def test_scriptmodule_GriffinLim(self):
+        tensor = torch.rand((1, 201, 6))
+        _test_script_module(transforms.GriffinLim, tensor, length=1000, rand_init=False)
+
     def test_mu_law_companding(self):
 
         quantization_channels = 256

torchaudio/functional.py

@@ -7,6 +7,7 @@
 __all__ = [
     "istft",
     "spectrogram",
+    "griffinlim",
     "amplitude_to_DB",
     "create_fb_matrix",
     "create_dct",
@@ -118,15 +119,15 @@ def istft(
     """
     stft_matrix_dim = stft_matrix.dim()
     assert 3 <= stft_matrix_dim, "Incorrect stft dimension: %d" % (stft_matrix_dim)
-    assert stft_matrix.nelement() > 0
+    assert stft_matrix.numel() > 0
 
     if stft_matrix_dim == 3:
         # add a channel dimension
         stft_matrix = stft_matrix.unsqueeze(0)
 
     # pack batch
     shape = stft_matrix.size()
-    stft_matrix = stft_matrix.reshape(-1, *shape[-3:])
+    stft_matrix = stft_matrix.reshape(-1, shape[-3], shape[-2], shape[-1])
 
     dtype = stft_matrix.dtype
     device = stft_matrix.device
@@ -151,7 +152,8 @@ def istft(
     assert 0 < win_length <= n_fft
 
     if window is None:
-        window = torch.ones(win_length, requires_grad=False, device=device, dtype=dtype)
+        window = torch.ones(win_length)
+        window.to(device=device, dtype=dtype)
 
     assert window.dim() == 1 and window.size(0) == win_length
 
@@ -174,9 +176,8 @@ def istft(
     # each column of a channel is a frame which needs to be overlap added at the right place
     ytmp = ytmp.transpose(1, 2)  # size (channel, n_fft, n_frame)
 
-    eye = torch.eye(n_fft, requires_grad=False, device=device, dtype=dtype).unsqueeze(
-        1
-    )  # size (n_fft, 1, n_fft)
+    eye = torch.eye(n_fft)
+    eye = eye.to(device=device, dtype=dtype).unsqueeze(1)  # size (n_fft, 1, n_fft)
 
     # this does overlap add where the frames of ytmp are added such that the i'th frame of
     # ytmp is added starting at i*hop_length in the output
@@ -270,6 +271,94 @@ def spectrogram(
     return spec_f
 
 
+def griffinlim(
+    spectrogram, window, n_fft, hop_length, win_length, power, normalized, n_iter, momentum, length, rand_init
+):
+    # type: (Tensor, Tensor, int, int, int, int, bool, int, float, Optional[int], bool) -> Tensor
+    r"""Compute waveform from a linear scale magnitude spectrogram using the Griffin-Lim transformation.
+        Implementation ported from `librosa`.
+
+    .. [1] McFee, Brian, Colin Raffel, Dawen Liang, Daniel PW Ellis, Matt McVicar, Eric Battenberg, and Oriol Nieto.
+        "librosa: Audio and music signal analysis in python."
+        In Proceedings of the 14th python in science conference, pp. 18-25. 2015.
+
+    .. [2] Perraudin, N., Balazs, P., & Søndergaard, P. L.
+        "A fast Griffin-Lim algorithm,"
+        IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (pp. 1-4),
+        Oct. 2013.
+
+    .. [3] D. W. Griffin and J. S. Lim,
+        "Signal estimation from modified short-time Fourier transform,"
+        IEEE Trans. ASSP, vol.32, no.2, pp.236–243, Apr. 1984.
+
+    Args:
+        spectrogram (torch.Tensor): A magnitude-only STFT spectrogram of dimension (channel, freq, frames)
+            where freq is ``n_fft // 2 + 1``.
+        window (torch.Tensor): Window tensor that is applied/multiplied to each frame/window
+        n_fft (int): Size of FFT, creates ``n_fft // 2 + 1`` bins
+        hop_length (int): Length of hop between STFT windows. (
+            Default: ``win_length // 2``)
+        win_length (int): Window size. (Default: ``n_fft``)
+        power (int): Exponent for the magnitude spectrogram,
+            (must be > 0) e.g., 1 for energy, 2 for power, etc. (Default: ``2``)
+        normalized (bool): Whether to normalize by magnitude after stft. (Default: ``False``)
+        n_iter (int): Number of iteration for phase recovery process.
+        momentum (float): The momentum parameter for fast Griffin-Lim.
+            Setting this to 0 recovers the original Griffin-Lim method.
+            Values near 1 can lead to faster convergence, but above 1 may not converge. (Default: 0.99)
+        length (Optional[int]): Array length of the expected output. (Default: ``None``)
+        rand_init (bool): Initializes phase randomly if True, to zero otherwise. (Default: ``True``)
+
+    Returns:
+        torch.Tensor: waveform of (channel, time), where time equals the ``length`` parameter if given.
+    """
+    assert momentum < 1, 'momentum=%s > 1 can be unstable' % momentum
+    assert momentum > 0, 'momentum=%s < 0' % momentum
+
+    spectrogram = spectrogram.pow(1 / power)
+
+    # randomly initialize the phase
+    batch, freq, frames = spectrogram.size()
+    if rand_init:
+        angles = 2 * math.pi * torch.rand(batch, freq, frames)
+    else:
+        angles = torch.zeros(batch, freq, frames)
+    angles = torch.stack([angles.cos(), angles.sin()], dim=-1) \
+                  .to(dtype=spectrogram.dtype, device=spectrogram.device)
+    spectrogram = spectrogram.unsqueeze(-1).expand_as(angles)
+
+    # And initialize the previous iterate to 0
+    rebuilt = torch.tensor(0.)
+
+    for _ in range(n_iter):
+        # Store the previous iterate
+        tprev = rebuilt
+
+        # Invert with our current estimate of the phases
+        inverse = istft(spectrogram * angles,
+                        n_fft=n_fft,
+                        hop_length=hop_length,
+                        win_length=win_length,
+                        window=window,
+                        length=length).float()
+
+        # Rebuild the spectrogram
+        rebuilt = _stft(inverse, n_fft, hop_length, win_length, window,
+                        True, 'reflect', False, True)
+
+        # Update our phase estimates
+        angles = rebuilt - tprev.mul_(momentum / (1 + momentum))
+        angles = angles.div_(complex_norm(angles).add_(1e-16).unsqueeze(-1).expand_as(angles))
+
+    # Return the final phase estimates
+    return istft(spectrogram * angles,
+                 n_fft=n_fft,
+                 hop_length=hop_length,
+                 win_length=win_length,
+                 window=window,
+                 length=length)
+
+
 def amplitude_to_DB(x, multiplier, amin, db_multiplier, top_db=None):
     # type: (Tensor, float, float, float, Optional[float]) -> Tensor
     r"""Turn a tensor from the power/amplitude scale to the decibel scale.

torchaudio/transforms.py

@@ -9,6 +9,7 @@
 
 __all__ = [
     'Spectrogram',
+    'GriffinLim',
     'AmplitudeToDB',
     'MelScale',
     'MelSpectrogram',
@@ -70,6 +71,69 @@ def forward(self, waveform):
                              self.win_length, self.power, self.normalized)
 
 
+class GriffinLim(torch.nn.Module):
+    r"""Compute waveform from a linear scale magnitude spectrogram using the Griffin-Lim transformation.
+        Implementation ported from `librosa`.
+
+    .. [1] McFee, Brian, Colin Raffel, Dawen Liang, Daniel PW Ellis, Matt McVicar, Eric Battenberg, and Oriol Nieto.
+        "librosa: Audio and music signal analysis in python."
+        In Proceedings of the 14th python in science conference, pp. 18-25. 2015.
+
+    .. [2] Perraudin, N., Balazs, P., & Søndergaard, P. L.
+        "A fast Griffin-Lim algorithm,"
+        IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (pp. 1-4),
+        Oct. 2013.
+
+    .. [3] D. W. Griffin and J. S. Lim,
+        "Signal estimation from modified short-time Fourier transform,"
+        IEEE Trans. ASSP, vol.32, no.2, pp.236–243, Apr. 1984.
+
+    Args:
+        n_fft (int, optional): Size of FFT, creates ``n_fft // 2 + 1`` bins
+        n_iter (int, optional): Number of iteration for phase recovery process.
+        win_length (int): Window size. (Default: ``n_fft``)
+        hop_length (int, optional): Length of hop between STFT windows. (
+            Default: ``win_length // 2``)
+        window_fn (Callable[[...], torch.Tensor]): A function to create a window tensor
+            that is applied/multiplied to each frame/window. (Default: ``torch.hann_window``)
+        power (int): Exponent for the magnitude spectrogram,
+            (must be > 0) e.g., 1 for energy, 2 for power, etc. (Default: ``2``)
+        normalized (bool): Whether to normalize by magnitude after stft. (Default: ``False``)
+        wkwargs (Dict[..., ...]): Arguments for window function. (Default: ``None``)
+        momentum (float): The momentum parameter for fast Griffin-Lim.
+            Setting this to 0 recovers the original Griffin-Lim method.
+            Values near 1 can lead to faster convergence, but above 1 may not converge. (Default: 0.99)
+        length (int, optional): Array length of the expected output. (Default: ``None``)
+        rand_init (bool): Initializes phase randomly if True and to zero otherwise. (Default: ``True``)
+    """
+    __constants__ = ['n_fft', 'n_iter', 'win_length', 'hop_length', 'power', 'normalized',
+                     'length', 'momentum', 'rand_init']
+
+    def __init__(self, n_fft=400, n_iter=32, win_length=None, hop_length=None,
+                 window_fn=torch.hann_window, power=2, normalized=False, wkwargs=None,
+                 momentum=0.99, length=None, rand_init=True):
+        super(GriffinLim, self).__init__()
+
+        assert momentum < 1, 'momentum=%s > 1 can be unstable' % momentum
+        assert momentum > 0, 'momentum=%s < 0' % momentum
+
+        self.n_fft = n_fft
+        self.n_iter = n_iter
+        self.win_length = win_length if win_length is not None else n_fft
+        self.hop_length = hop_length if hop_length is not None else self.win_length // 2
+        window = window_fn(self.win_length) if wkwargs is None else window_fn(self.win_length, **wkwargs)
+        self.register_buffer('window', window)
+        self.normalized = normalized
+        self.length = length
+        self.power = power
+        self.momentum = momentum / (1 + momentum)
+        self.rand_init = rand_init
+
+    def forward(self, S):
+        return F.griffinlim(S, self.window, self.n_fft, self.hop_length, self.win_length, self.power,
+                            self.normalized, self.n_iter, self.momentum, self.length, self.rand_init)
+
+
 class AmplitudeToDB(torch.jit.ScriptModule):
     r"""Turn a tensor from the power/amplitude scale to the decibel scale.