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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.
import torch
import torch.nn.functional as F
import torch.nn as nn
from modules.vocoder_blocks import *
LRELU_SLOPE = 0.1
class ISTFT(nn.Module):
"""
Custom implementation of ISTFT since torch.istft doesn't allow custom padding (other than `center=True`) with
windowing. This is because the NOLA (Nonzero Overlap Add) check fails at the edges.
See issue: https://github.com/pytorch/pytorch/issues/62323
Specifically, in the context of neural vocoding we are interested in "same" padding analogous to CNNs.
The NOLA constraint is met as we trim padded samples anyway.
Args:
n_fft (int): Size of Fourier transform.
hop_length (int): The distance between neighboring sliding window frames.
win_length (int): The size of window frame and STFT filter.
padding (str, optional): Type of padding. Options are "center" or "same". Defaults to "same".
"""
def __init__(
self,
n_fft: int,
hop_length: int,
win_length: int,
padding: str = "same",
):
super().__init__()
if padding not in ["center", "same"]:
raise ValueError("Padding must be 'center' or 'same'.")
self.padding = padding
self.n_fft = n_fft
self.hop_length = hop_length
self.win_length = win_length
def forward(self, spec: torch.Tensor, window) -> torch.Tensor:
"""
Compute the Inverse Short Time Fourier Transform (ISTFT) of a complex spectrogram.
Args:
spec (Tensor): Input complex spectrogram of shape (B, N, T), where B is the batch size,
N is the number of frequency bins, and T is the number of time frames.
Returns:
Tensor: Reconstructed time-domain signal of shape (B, L), where L is the length of the output signal.
"""
if self.padding == "center":
# Fallback to pytorch native implementation
return torch.istft(
spec,
self.n_fft,
self.hop_length,
self.win_length,
window,
center=True,
)
elif self.padding == "same":
pad = (self.win_length - self.hop_length) // 2
else:
raise ValueError("Padding must be 'center' or 'same'.")
assert spec.dim() == 3, "Expected a 3D tensor as input"
B, N, T = spec.shape
# Inverse FFT
ifft = torch.fft.irfft(spec, self.n_fft, dim=1, norm="backward")
ifft = ifft * window[None, :, None]
# Overlap and Add
output_size = (T - 1) * self.hop_length + self.win_length
y = torch.nn.functional.fold(
ifft,
output_size=(1, output_size),
kernel_size=(1, self.win_length),
stride=(1, self.hop_length),
)[:, 0, 0, pad:-pad]
# Window envelope
window_sq = window.square().expand(1, T, -1).transpose(1, 2)
window_envelope = torch.nn.functional.fold(
window_sq,
output_size=(1, output_size),
kernel_size=(1, self.win_length),
stride=(1, self.hop_length),
).squeeze()[pad:-pad]
# Normalize
assert (window_envelope > 1e-11).all()
y = y / window_envelope
return y
# The ASP and PSP Module are adopted from APNet under the MIT License
# https://github.com/YangAi520/APNet/blob/main/models.py
class ASPResBlock(torch.nn.Module):
def __init__(self, cfg, channels, kernel_size=3, dilation=(1, 3, 5)):
super(ASPResBlock, self).__init__()
self.cfg = cfg
self.convs1 = nn.ModuleList(
[
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation[0],
padding=get_padding(kernel_size, dilation[0]),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation[1],
padding=get_padding(kernel_size, dilation[1]),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation[2],
padding=get_padding(kernel_size, dilation[2]),
)
),
]
)
self.convs1.apply(init_weights)
self.convs2 = nn.ModuleList(
[
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=1,
padding=get_padding(kernel_size, 1),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=1,
padding=get_padding(kernel_size, 1),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=1,
padding=get_padding(kernel_size, 1),
)
),
]
)
self.convs2.apply(init_weights)
def forward(self, x):
for c1, c2 in zip(self.convs1, self.convs2):
xt = F.leaky_relu(x, LRELU_SLOPE)
xt = c1(xt)
xt = F.leaky_relu(xt, LRELU_SLOPE)
xt = c2(xt)
x = xt + x
return x
class PSPResBlock(torch.nn.Module):
def __init__(self, cfg, channels, kernel_size=3, dilation=(1, 3, 5)):
super(PSPResBlock, self).__init__()
self.cfg = cfg
self.convs1 = nn.ModuleList(
[
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation[0],
padding=get_padding(kernel_size, dilation[0]),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation[1],
padding=get_padding(kernel_size, dilation[1]),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation[2],
padding=get_padding(kernel_size, dilation[2]),
)
),
]
)
self.convs1.apply(init_weights)
self.convs2 = nn.ModuleList(
[
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=1,
padding=get_padding(kernel_size, 1),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=1,
padding=get_padding(kernel_size, 1),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=1,
padding=get_padding(kernel_size, 1),
)
),
]
)
self.convs2.apply(init_weights)
def forward(self, x):
for c1, c2 in zip(self.convs1, self.convs2):
xt = F.leaky_relu(x, LRELU_SLOPE)
xt = c1(xt)
xt = F.leaky_relu(xt, LRELU_SLOPE)
xt = c2(xt)
x = xt + x
return x
class APNet(torch.nn.Module):
def __init__(self, cfg):
super(APNet, self).__init__()
self.cfg = cfg
self.ASP_num_kernels = len(cfg.model.apnet.ASP_resblock_kernel_sizes)
self.PSP_num_kernels = len(cfg.model.apnet.PSP_resblock_kernel_sizes)
self.ASP_input_conv = weight_norm(
Conv1d(
cfg.preprocess.n_mel,
cfg.model.apnet.ASP_channel,
cfg.model.apnet.ASP_input_conv_kernel_size,
1,
padding=get_padding(cfg.model.apnet.ASP_input_conv_kernel_size, 1),
)
)
self.PSP_input_conv = weight_norm(
Conv1d(
cfg.preprocess.n_mel,
cfg.model.apnet.PSP_channel,
cfg.model.apnet.PSP_input_conv_kernel_size,
1,
padding=get_padding(cfg.model.apnet.PSP_input_conv_kernel_size, 1),
)
)
self.ASP_ResNet = nn.ModuleList()
for j, (k, d) in enumerate(
zip(
cfg.model.apnet.ASP_resblock_kernel_sizes,
cfg.model.apnet.ASP_resblock_dilation_sizes,
)
):
self.ASP_ResNet.append(ASPResBlock(cfg, cfg.model.apnet.ASP_channel, k, d))
self.PSP_ResNet = nn.ModuleList()
for j, (k, d) in enumerate(
zip(
cfg.model.apnet.PSP_resblock_kernel_sizes,
cfg.model.apnet.PSP_resblock_dilation_sizes,
)
):
self.PSP_ResNet.append(PSPResBlock(cfg, cfg.model.apnet.PSP_channel, k, d))
self.ASP_output_conv = weight_norm(
Conv1d(
cfg.model.apnet.ASP_channel,
cfg.preprocess.n_fft // 2 + 1,
cfg.model.apnet.ASP_output_conv_kernel_size,
1,
padding=get_padding(cfg.model.apnet.ASP_output_conv_kernel_size, 1),
)
)
self.PSP_output_R_conv = weight_norm(
Conv1d(
cfg.model.apnet.PSP_channel,
cfg.preprocess.n_fft // 2 + 1,
cfg.model.apnet.PSP_output_R_conv_kernel_size,
1,
padding=get_padding(cfg.model.apnet.PSP_output_R_conv_kernel_size, 1),
)
)
self.PSP_output_I_conv = weight_norm(
Conv1d(
cfg.model.apnet.PSP_channel,
cfg.preprocess.n_fft // 2 + 1,
cfg.model.apnet.PSP_output_I_conv_kernel_size,
1,
padding=get_padding(cfg.model.apnet.PSP_output_I_conv_kernel_size, 1),
)
)
self.iSTFT = ISTFT(
self.cfg.preprocess.n_fft,
hop_length=self.cfg.preprocess.hop_size,
win_length=self.cfg.preprocess.win_size,
)
self.ASP_output_conv.apply(init_weights)
self.PSP_output_R_conv.apply(init_weights)
self.PSP_output_I_conv.apply(init_weights)
def forward(self, mel):
logamp = self.ASP_input_conv(mel)
logamps = None
for j in range(self.ASP_num_kernels):
if logamps is None:
logamps = self.ASP_ResNet[j](logamp)
else:
logamps += self.ASP_ResNet[j](logamp)
logamp = logamps / self.ASP_num_kernels
logamp = F.leaky_relu(logamp)
logamp = self.ASP_output_conv(logamp)
pha = self.PSP_input_conv(mel)
phas = None
for j in range(self.PSP_num_kernels):
if phas is None:
phas = self.PSP_ResNet[j](pha)
else:
phas += self.PSP_ResNet[j](pha)
pha = phas / self.PSP_num_kernels
pha = F.leaky_relu(pha)
R = self.PSP_output_R_conv(pha)
I = self.PSP_output_I_conv(pha)
pha = torch.atan2(I, R)
rea = torch.exp(logamp) * torch.cos(pha)
imag = torch.exp(logamp) * torch.sin(pha)
spec = torch.cat((rea.unsqueeze(-1), imag.unsqueeze(-1)), -1)
spec = torch.view_as_complex(spec)
audio = self.iSTFT.forward(
spec, torch.hann_window(self.cfg.preprocess.win_size).to(mel.device)
)
return logamp, pha, rea, imag, audio.unsqueeze(1)
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