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# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#################### NSF ####################
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
# This code is adopted from Xin Wang's NSF under the MIT License
# https://github.com/nii-yamagishilab/project-NN-Pytorch-scripts
class SineGen(nn.Module):
def __init__(
self, fs, harmonic_num=0, amp=0.1, noise_std=0.003, voiced_threshold=0
):
super(SineGen, self).__init__()
self.amp = amp
self.noise_std = noise_std
self.harmonic_num = harmonic_num
self.dim = harmonic_num + 1
self.fs = fs
self.voice_threshold = voiced_threshold
def _f0toUnvoiced(self, f0):
uv = torch.ones_like(f0)
uv = uv * (f0 > self.voice_threshold)
return uv
@torch.no_grad()
def forward(self, f0, upp):
f0 = f0.unsqueeze(-1)
fn = torch.multiply(
f0, torch.arange(1, self.dim + 1, device=f0.device).reshape(1, 1, -1)
)
rad_values = (fn / self.fs) % 1
rand_ini = torch.rand(fn.shape[0], fn.shape[2], device=fn.device)
rand_ini[:, 0] = 0
rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
is_half = rad_values.dtype is not torch.float32
tmp_over_one = torch.cumsum(rad_values.double(), 1)
if is_half:
tmp_over_one = tmp_over_one.half()
else:
tmp_over_one = tmp_over_one.float()
tmp_over_one *= upp
tmp_over_one = F.interpolate(
tmp_over_one.transpose(2, 1),
scale_factor=upp,
mode="linear",
align_corners=True,
).transpose(2, 1)
rad_values = F.interpolate(
rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
).transpose(2, 1)
tmp_over_one %= 1
tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
cumsum_shift = torch.zeros_like(rad_values)
cumsum_shift[:, 1:, :] = tmp_over_one_idx * (-1.0)
rad_values = rad_values.double()
cumsum_shift = cumsum_shift.double()
sine_waves = torch.sin(
torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
)
if is_half:
sine_waves = sine_waves.half()
else:
sine_waves = sine_waves.float()
sine_waves = sine_waves * self.amp
uv = self._f0toUnvoiced(f0)
uv = F.interpolate(
uv.transpose(2, 1), scale_factor=upp, mode="nearest"
).transpose(2, 1)
noise_amp = uv * self.noise_std + (1 - uv) * self.amp / 3
noise = noise_amp * torch.randn_like(sine_waves)
sine_waves = sine_waves * uv + noise
return sine_waves, uv, noise
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