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# Copyright 2022 SPTK Working Group #
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# you may not use this file except in compliance with the License. #
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# http://www.apache.org/licenses/LICENSE-2.0 #
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import torch
from torch import nn
import torch.nn.functional as F
from ..misc.utils import TWO_PI
from ..misc.utils import UNVOICED_SYMBOL
from .linear_intpl import LinearInterpolation
[docs]
class ExcitationGeneration(nn.Module):
"""See `this page <https://sp-nitech.github.io/sptk/latest/main/excite.html>`_
for details.
Parameters
----------
frame_period : int >= 1
Frame period in samples, :math:`P`.
voiced_region : ['pulse', 'sinusoidal', 'sawtooth']
Value on voiced region.
unvoiced_region : ['gauss', 'zeros']
Value on unvoiced region.
"""
def __init__(self, frame_period, voiced_region="pulse", unvoiced_region="gauss"):
super().__init__()
assert 1 <= frame_period
assert voiced_region in ("pulse", "sinusoidal", "sawtooth")
assert unvoiced_region in ("gauss", "zeros")
self.frame_period = frame_period
self.voiced_region = voiced_region
self.unvoiced_region = unvoiced_region
[docs]
def forward(self, p):
"""Generate a simple excitation signal.
Parameters
----------
p : Tensor [shape=(..., N)]
Pitch in seconds.
Returns
-------
out : Tensor [shape=(..., NxP)]
Excitation signal.
Examples
--------
>>> p = torch.tensor([2.0, 3.0])
>>> excite = diffsptk.ExcitationGeneration(3)
>>> e = excite(p)
>>> e
tensor([1.4142, 0.0000, 1.6330, 0.0000, 0.0000, 1.7321])
"""
return self._forward(
p, self.frame_period, self.voiced_region, self.unvoiced_region
)
@staticmethod
def _forward(p, frame_period, voiced_region, unvoiced_region):
# Make mask represents voiced region.
base_mask = torch.clip(p, min=0, max=1)
mask = torch.ne(base_mask, UNVOICED_SYMBOL)
mask = torch.repeat_interleave(mask, frame_period, dim=-1)
# Extend right side for interpolation.
tmp_mask = F.pad(base_mask, (1, 0))
tmp_mask = torch.eq(tmp_mask[..., 1:] - tmp_mask[..., :-1], -1)
p[tmp_mask] = torch.roll(p, 1, dims=-1)[tmp_mask]
# Interpolate pitch.
if p.dim() != 1:
p = p.transpose(-2, -1)
p = LinearInterpolation._func(p, frame_period)
if p.dim() != 1:
p = p.transpose(-2, -1)
p *= mask
# Compute phase.
voiced_pos = torch.gt(p, 0)
q = torch.zeros_like(p)
q[voiced_pos] = torch.reciprocal(p[voiced_pos])
s = torch.cumsum(q.double(), dim=-1)
bias, _ = torch.cummax(s * ~mask, dim=-1)
phase = (s - bias).to(p.dtype)
if voiced_region == "pulse":
r = torch.ceil(phase)
r = F.pad(r, (1, 0))
pulse_pos = torch.ge(r[..., 1:] - r[..., :-1], 1)
e = torch.zeros_like(p)
e[pulse_pos] = torch.sqrt(p[pulse_pos])
elif voiced_region == "sinusoidal":
e = torch.sin(TWO_PI * phase)
elif voiced_region == "sawtooth":
e = torch.fmod(phase, 2) - 1
else:
raise ValueError(f"voiced_region {voiced_region} is not supported.")
if unvoiced_region == "gauss":
e[~mask] = torch.randn(torch.sum(~mask), device=e.device)
elif unvoiced_region == "zeros":
pass
else:
raise ValueError(f"unvoiced_region {unvoiced_region} is not supported.")
return e
_func = _forward