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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 torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d | |
| from torch.nn.utils import weight_norm, spectral_norm | |
| 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) | |