Merge pull request #77 from sp-nitech/csm

Add acr2csm

.github/workflows/ci.yml

@@ -18,8 +18,8 @@ jobs:
       matrix:
         include:
           - python: 3.8
-            torch: 1.11.0
-            torchaudio: 0.11.0
+            torch: 1.12.0
+            torchaudio: 0.12.0
           - python: 3.12
             torch: 2.3.1
             torchaudio: 2.3.1

Makefile

@@ -18,8 +18,8 @@ PROJECT := diffsptk
 MODULE  :=
 
 PYTHON_VERSION     := 3.9
-TORCH_VERSION      := 1.11.0
-TORCHAUDIO_VERSION := 0.11.0
+TORCH_VERSION      := 1.12.0
+TORCHAUDIO_VERSION := 0.12.0
 PLATFORM           := cu113
 
 venv:

README.md

@@ -6,7 +6,7 @@
 [![Stable Manual](https://img.shields.io/badge/docs-stable-blue.svg)](https://sp-nitech.github.io/diffsptk/2.0.1/)
 [![Downloads](https://static.pepy.tech/badge/diffsptk)](https://pepy.tech/project/diffsptk)
 [![Python Version](https://img.shields.io/pypi/pyversions/diffsptk.svg)](https://pypi.python.org/pypi/diffsptk)
-[![PyTorch Version](https://img.shields.io/badge/pytorch-1.11.0%20%7C%202.3.1-orange.svg)](https://pypi.python.org/pypi/diffsptk)
+[![PyTorch Version](https://img.shields.io/badge/pytorch-1.12.0%20%7C%202.3.1-orange.svg)](https://pypi.python.org/pypi/diffsptk)
 [![PyPI Version](https://img.shields.io/pypi/v/diffsptk.svg)](https://pypi.python.org/pypi/diffsptk)
 [![Codecov](https://codecov.io/gh/sp-nitech/diffsptk/branch/master/graph/badge.svg)](https://app.codecov.io/gh/sp-nitech/diffsptk)
 [![License](https://img.shields.io/github/license/sp-nitech/diffsptk.svg)](https://github.com/sp-nitech/diffsptk/blob/master/LICENSE)
@@ -16,7 +16,7 @@
 ## Requirements
 
 - Python 3.8+
-- PyTorch 1.11.0+
+- PyTorch 1.12.0+
 
 ## Documentation
 

diffsptk/functional.py

@@ -45,6 +45,23 @@ def acorr(x, acr_order, norm=False, estimator="none"):
     )
 
 
+def acr2csm(r):
+    """Convert autocorrelation to CSM coefficients.
+
+    Parameters
+    ----------
+    r : Tensor [shape=(..., M+1)]
+        Autocorrelation.
+
+    Returns
+    -------
+    out : Tensor [shape=(..., M+1)]
+        CSM coefficients.
+
+    """
+    return nn.AutocorrelationToCompositeSinusoidalModelCoefficients._func(r)
+
+
 def alaw(x, abs_max=1, a=87.6):
     """Compress waveform by A-law algorithm.
 

diffsptk/misc/utils.py

@@ -211,8 +211,13 @@ def symmetric_toeplitz(x):
 
 def hankel(x):
     d = x.size(-1)
-    assert d % 2 == 1
-    X = x.unfold(-1, (d + 1) // 2, 1)
+    n = (d + 1) // 2
+    X = x.unfold(-1, n, 1)[..., :n, :]
+    return X
+
+
+def vander(x):
+    X = torch.linalg.vander(x).transpose(-2, -1)
     return X
 
 

diffsptk/modules/__init__.py

@@ -1,4 +1,5 @@
 from .acorr import Autocorrelation
+from .acr2csm import AutocorrelationToCompositeSinusoidalModelCoefficients
 from .alaw import ALawCompression
 from .ap import Aperiodicity
 from .b2mc import MLSADigitalFilterCoefficientsToMelCepstrum

diffsptk/modules/acr2csm.py

@@ -0,0 +1,118 @@
+# ------------------------------------------------------------------------ #
+# Copyright 2022 SPTK Working Group                                        #
+#                                                                          #
+# Licensed under the Apache License, Version 2.0 (the "License");          #
+# you may not use this file except in compliance with the License.         #
+# You may obtain a copy of the License at                                  #
+#                                                                          #
+#     http://www.apache.org/licenses/LICENSE-2.0                           #
+#                                                                          #
+# Unless required by applicable law or agreed to in writing, software      #
+# distributed under the License is distributed on an "AS IS" BASIS,        #
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #
+# See the License for the specific language governing permissions and      #
+# limitations under the License.                                           #
+# ------------------------------------------------------------------------ #
+
+from scipy.special import comb
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+from ..misc.utils import check_size
+from ..misc.utils import hankel
+from ..misc.utils import to
+from ..misc.utils import vander
+from .root_pol import PolynomialToRoots
+
+
+class AutocorrelationToCompositeSinusoidalModelCoefficients(nn.Module):
+    """See `this page <https://sp-nitech.github.io/sptk/latest/main/acr2csm.html>`_
+    for details.
+
+    Parameters
+    ----------
+    csm_order : int >= 0
+        Order of CSM coefficients, :math:`M`.
+
+    """
+
+    def __init__(self, csm_order):
+        super().__init__()
+
+        assert 1 <= csm_order
+        assert csm_order % 2 == 1
+
+        self.csm_order = csm_order
+        self.register_buffer("C", self._precompute(self.csm_order))
+
+    def forward(self, r):
+        """Convert autocorrelation to CSM coefficients.
+
+        Parameters
+        ----------
+        r : Tensor [shape=(..., M+1)]
+            Autocorrelation.
+
+        Returns
+        -------
+        out : Tensor [shape=(..., M+1)]
+            Composite sinusoidal model coefficients.
+
+        Examples
+        --------
+        >>> x = diffsptk.nrand(4)
+        >>> x
+        tensor([ 0.0165, -2.3693,  0.1375, -0.2262,  1.3307])
+        >>> acorr = diffsptk.Autocorrelation(5, 3)
+        >>> acr2csm = diffsptk.AutocorrelationToCompositeSinusoidalModelCoefficients(3)
+        >>> c = acr2csm(acorr(x))
+        >>> c
+        tensor([0.9028, 2.5877, 3.8392, 3.6153])
+
+        """
+        check_size(r.size(-1), self.csm_order + 1, "dimension of autocorrelation")
+        return self._forward(r, self.C)
+
+    @staticmethod
+    def _forward(r, C):
+        u = torch.matmul(r, C)
+        u1, u2 = torch.tensor_split(u, 2, dim=-1)
+
+        U = hankel(-u)
+        p = torch.matmul(U.inverse(), u2.unsqueeze(-1)).squeeze(-1)
+        x = PolynomialToRoots._func(F.pad(p.flip(-1), (1, 0), value=1))
+        x, _ = torch.sort(x.real, descending=True)
+        w = torch.acos(x)
+
+        V = vander(x)
+        m = torch.matmul(V.inverse(), u1.unsqueeze(-1)).squeeze(-1)
+        csm = torch.cat((w, m), dim=-1)
+        return csm
+
+    @staticmethod
+    def _func(r):
+        C = AutocorrelationToCompositeSinusoidalModelCoefficients._precompute(
+            r.size(-1) - 1, dtype=r.dtype, device=r.device
+        )
+        return AutocorrelationToCompositeSinusoidalModelCoefficients._forward(r, C)
+
+    @staticmethod
+    def _precompute(csm_order, dtype=None, device=None):
+        N = csm_order + 1
+        B = torch.zeros((N, N), dtype=torch.double, device=device)
+        for n in range(N):
+            z = 2**-n
+            for k in range(n + 1):
+                B[k, n] = comb(n, k, exact=True) * z
+
+        C = torch.zeros((N, N), dtype=torch.double, device=device)
+        for k in range(N):
+            bias = k % 2
+            center = k // 2
+            length = center + 1
+            C[bias : bias + 2 * length : 2, k] = B[
+                bias + center : bias + center + length, k
+            ]
+        C[1:] *= 2
+        return to(C, dtype=dtype)

diffsptk/modules/levdur.py

@@ -55,6 +55,7 @@ def forward(self, r):
         Examples
         --------
         >>> x = diffsptk.nrand(4)
+        >>> x
         tensor([ 0.8226, -0.0284, -0.5715,  0.2127,  0.1217])
         >>> acorr = diffsptk.Autocorrelation(5, 2)
         >>> levdur = diffsptk.LevinsonDurbin(2)

docs/modules/acr2csm.rst

@@ -0,0 +1,11 @@
+.. _acr2csm:
+
+acr2csm
+-------
+
+.. autoclass:: diffsptk.AutocorrelationToCompositeSinusoidalModelCoefficients
+   :members:
+
+.. autofunction:: diffsptk.functional.acr2csm
+
+.. seealso:: :ref:`acorr`

pyproject.toml

@@ -25,6 +25,7 @@ classifiers = [
 ]
 dependencies = [
   "numpy < 2.0.0",
+  "scipy < 1.14.0",
   "librosa >= 0.10.1",
   "soundfile >= 0.10.2",
   "torch >= 1.11.0",

tests/test_acr2csm.py

@@ -0,0 +1,45 @@
+# ------------------------------------------------------------------------ #
+# Copyright 2022 SPTK Working Group                                        #
+#                                                                          #
+# Licensed under the Apache License, Version 2.0 (the "License");          #
+# you may not use this file except in compliance with the License.         #
+# You may obtain a copy of the License at                                  #
+#                                                                          #
+#     http://www.apache.org/licenses/LICENSE-2.0                           #
+#                                                                          #
+# Unless required by applicable law or agreed to in writing, software      #
+# distributed under the License is distributed on an "AS IS" BASIS,        #
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #
+# See the License for the specific language governing permissions and      #
+# limitations under the License.                                           #
+# ------------------------------------------------------------------------ #
+
+import pytest
+
+import diffsptk
+import tests.utils as U
+
+
+@pytest.mark.parametrize("device", ["cpu", "cuda"])
+@pytest.mark.parametrize("module", [False, True])
+def test_compatibility(device, module, M=25, L=100, B=2):
+    acr2csm = U.choice(
+        module,
+        diffsptk.AutocorrelationToCompositeSinusoidalModelCoefficients,
+        diffsptk.functional.acr2csm,
+        {"csm_order": M},
+    )
+
+    U.check_compatibility(
+        device,
+        acr2csm,
+        [],
+        f"nrand -l {B*L} | acorr -m {M} -l {L}",
+        f"acr2csm -m {M}",
+        [],
+        dx=M + 1,
+        dy=M + 1,
+    )
+
+    acorr = diffsptk.Autocorrelation(L, M)
+    U.check_differentiability(device, [acr2csm, acorr], [B, L])