Fix excite and remove hilbert2

Makefile

@@ -85,8 +85,8 @@ tool-clean:
 
 update: tool
 	. .venv/bin/activate && python -m pip install --upgrade pip
-	@for package in $$(./tools/taplo/taplo get -f pyproject.toml project.optional-dependencies.dev); do \
-		. .venv/bin/activate && python -m pip install --upgrade $$package; \
+	@./tools/taplo/taplo get -f pyproject.toml project.optional-dependencies.dev | while read -r package; do \
+		. .venv/bin/activate && python -m pip install --upgrade "$$package"; \
 	done
 
 clean: dist-clean doc-clean test-clean tool-clean

diffsptk/functional.py

@@ -1138,33 +1138,6 @@ def hilbert(x: Tensor, fft_length: int | None = None, dim: int = -1) -> Tensor:
     return nn.HilbertTransform._func(x, fft_length=fft_length, dim=dim)
 
 
-def hilbert2(
-    x: Tensor,
-    fft_length: ArrayLike[int] | int | None = None,
-    dim: ArrayLike[int] = (-2, -1),
-) -> Tensor:
-    """Compute the analytic signal using the Hilbert transform.
-
-    Parameters
-    ----------
-    x : Tensor [shape=(..., T1, T2, ...)]
-        The input signal.
-
-    fft_length : int, list[int], or None
-        The number of FFT bins. If None, set to (:math:`T1`, :math:`T2`).
-
-    dim : list[int]
-        The dimensions along which to take the Hilbert transform.
-
-    Returns
-    -------
-    out : Tensor [shape=(..., T1, T2, ...)]
-        The analytic signal.
-
-    """
-    return nn.TwoDimensionalHilbertTransform._func(x, fft_length=fft_length, dim=dim)
-
-
 def histogram(
     x: Tensor,
     n_bin: int = 10,

diffsptk/modules/__init__.py

@@ -62,7 +62,6 @@
 from .griffin import GriffinLim
 from .grpdelay import GroupDelay
 from .hilbert import HilbertTransform
-from .hilbert2 import TwoDimensionalHilbertTransform
 from .histogram import Histogram
 from .ialaw import ALawExpansion
 from .ica import IndependentComponentAnalysis

diffsptk/modules/excite.py

@@ -148,15 +148,16 @@ def _forward(
         s = torch.cumsum(q.double(), dim=-1)
         bias, _ = torch.cummax(s * ~mask, dim=-1)
         phase = (s - bias).to(p.dtype)
-        if isinstance(init_phase, str):
-            if init_phase == "zeros":
-                pass
-            elif init_phase == "random":
-                phase += torch.rand_like(p[..., :1])
-            else:
-                raise ValueError(f"init_phase {init_phase} is not supported.")
+        if not isinstance(init_phase, str):
+            shift = init_phase / TAU
+        elif init_phase == "zeros":
+            shift = 0.0
+        elif init_phase == "random":
+            shift = torch.rand_like(p[..., :1])
         else:
-            phase += init_phase / TAU
+            raise ValueError(f"init_phase {init_phase} is not supported.")
+        if isinstance(shift, torch.Tensor) or shift != 0.0:
+            phase += shift
 
         # Generate excitation signal using phase.
         if polarity == "auto":
@@ -170,15 +171,19 @@ def _forward(
 
             def get_pulse_pos(p):
                 r = torch.ceil(p)
-                r = F.pad(r, (1, 0))
                 return torch.ge(torch.diff(r), 1)
 
+            if isinstance(shift, float):
+                padded_phase = F.pad(phase, (1, 0), value=shift)
+            else:
+                padded_phase = torch.cat([shift, phase], dim=-1)
+
             if unipolar:
-                pulse_pos = get_pulse_pos(phase)
+                pulse_pos = get_pulse_pos(padded_phase)
                 e[pulse_pos] = torch.sqrt(p[pulse_pos])
             else:
-                pulse_pos1 = get_pulse_pos(phase)
-                pulse_pos2 = get_pulse_pos(0.5 * phase)
+                pulse_pos1 = get_pulse_pos(padded_phase)
+                pulse_pos2 = get_pulse_pos(0.5 * padded_phase)
                 e[pulse_pos1] = torch.sqrt(p[pulse_pos1])
                 e[pulse_pos1 & ~pulse_pos2] *= -1
         elif voiced_region == "sinusoidal":

docs/source/modules/hilbert.rst

@@ -7,7 +7,3 @@ hilbert
     :members:
 
 .. autofunction:: diffsptk.functional.hilbert
-
-.. seealso::
-
-    :ref:`hilbert2`