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| import torch | |
| from torch.nn.utils import remove_weight_norm | |
| from torch.nn.utils.parametrizations import weight_norm | |
| from typing import Optional | |
| from rvc.lib.algorithm.residuals import LRELU_SLOPE, ResBlock1, ResBlock2 | |
| from rvc.lib.algorithm.commons import init_weights | |
| class Generator(torch.nn.Module): | |
| """Generator for synthesizing audio. | |
| Args: | |
| initial_channel (int): Number of channels in the initial convolutional layer. | |
| resblock (str): Type of residual block to use (1 or 2). | |
| resblock_kernel_sizes (list): Kernel sizes of the residual blocks. | |
| resblock_dilation_sizes (list): Dilation rates of the residual blocks. | |
| upsample_rates (list): Upsampling rates. | |
| upsample_initial_channel (int): Number of channels in the initial upsampling layer. | |
| upsample_kernel_sizes (list): Kernel sizes of the upsampling layers. | |
| gin_channels (int, optional): Number of channels for the global conditioning input. Defaults to 0. | |
| """ | |
| def __init__( | |
| self, | |
| initial_channel, | |
| resblock, | |
| resblock_kernel_sizes, | |
| resblock_dilation_sizes, | |
| upsample_rates, | |
| upsample_initial_channel, | |
| upsample_kernel_sizes, | |
| gin_channels=0, | |
| ): | |
| super(Generator, self).__init__() | |
| self.num_kernels = len(resblock_kernel_sizes) | |
| self.num_upsamples = len(upsample_rates) | |
| self.conv_pre = torch.nn.Conv1d( | |
| initial_channel, upsample_initial_channel, 7, 1, padding=3 | |
| ) | |
| resblock = ResBlock1 if resblock == "1" else ResBlock2 | |
| self.ups = torch.nn.ModuleList() | |
| for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): | |
| self.ups.append( | |
| weight_norm( | |
| torch.nn.ConvTranspose1d( | |
| upsample_initial_channel // (2**i), | |
| upsample_initial_channel // (2 ** (i + 1)), | |
| k, | |
| u, | |
| padding=(k - u) // 2, | |
| ) | |
| ) | |
| ) | |
| self.resblocks = torch.nn.ModuleList() | |
| for i in range(len(self.ups)): | |
| ch = upsample_initial_channel // (2 ** (i + 1)) | |
| for j, (k, d) in enumerate( | |
| zip(resblock_kernel_sizes, resblock_dilation_sizes) | |
| ): | |
| self.resblocks.append(resblock(ch, k, d)) | |
| self.conv_post = torch.nn.Conv1d(ch, 1, 7, 1, padding=3, bias=False) | |
| self.ups.apply(init_weights) | |
| if gin_channels != 0: | |
| self.cond = torch.nn.Conv1d(gin_channels, upsample_initial_channel, 1) | |
| def forward(self, x: torch.Tensor, g: Optional[torch.Tensor] = None): | |
| x = self.conv_pre(x) | |
| if g is not None: | |
| x = x + self.cond(g) | |
| for i in range(self.num_upsamples): | |
| x = torch.nn.functional.leaky_relu(x, LRELU_SLOPE) | |
| x = self.ups[i](x) | |
| xs = None | |
| for j in range(self.num_kernels): | |
| if xs is None: | |
| xs = self.resblocks[i * self.num_kernels + j](x) | |
| else: | |
| xs += self.resblocks[i * self.num_kernels + j](x) | |
| x = xs / self.num_kernels | |
| x = torch.nn.functional.leaky_relu(x) | |
| x = self.conv_post(x) | |
| x = torch.tanh(x) | |
| return x | |
| def __prepare_scriptable__(self): | |
| """Prepares the module for scripting.""" | |
| for l in self.ups: | |
| for hook in l._forward_pre_hooks.values(): | |
| if ( | |
| hook.__module__ == "torch.nn.utils.parametrizations.weight_norm" | |
| and hook.__class__.__name__ == "WeightNorm" | |
| ): | |
| torch.nn.utils.remove_weight_norm(l) | |
| for l in self.resblocks: | |
| for hook in l._forward_pre_hooks.values(): | |
| if ( | |
| hook.__module__ == "torch.nn.utils.parametrizations.weight_norm" | |
| and hook.__class__.__name__ == "WeightNorm" | |
| ): | |
| torch.nn.utils.remove_weight_norm(l) | |
| return self | |
| def remove_weight_norm(self): | |
| """Removes weight normalization from the upsampling and residual blocks.""" | |
| for l in self.ups: | |
| remove_weight_norm(l) | |
| for l in self.resblocks: | |
| l.remove_weight_norm() | |
| class SineGen(torch.nn.Module): | |
| """Sine wave generator. | |
| Args: | |
| samp_rate (int): Sampling rate in Hz. | |
| harmonic_num (int, optional): Number of harmonic overtones. Defaults to 0. | |
| sine_amp (float, optional): Amplitude of sine waveform. Defaults to 0.1. | |
| noise_std (float, optional): Standard deviation of Gaussian noise. Defaults to 0.003. | |
| voiced_threshold (float, optional): F0 threshold for voiced/unvoiced classification. Defaults to 0. | |
| flag_for_pulse (bool, optional): Whether this SineGen is used inside PulseGen. Defaults to False. | |
| """ | |
| def __init__( | |
| self, | |
| samp_rate, | |
| harmonic_num=0, | |
| sine_amp=0.1, | |
| noise_std=0.003, | |
| voiced_threshold=0, | |
| flag_for_pulse=False, | |
| ): | |
| super(SineGen, self).__init__() | |
| self.sine_amp = sine_amp | |
| self.noise_std = noise_std | |
| self.harmonic_num = harmonic_num | |
| self.dim = self.harmonic_num + 1 | |
| self.sample_rate = samp_rate | |
| self.voiced_threshold = voiced_threshold | |
| def _f02uv(self, f0): | |
| """Converts F0 to voiced/unvoiced signal. | |
| Args: | |
| f0 (torch.Tensor): F0 tensor with shape (batch_size, length, 1).. | |
| """ | |
| # generate uv signal | |
| uv = torch.ones_like(f0) | |
| uv = uv * (f0 > self.voiced_threshold) | |
| return uv | |
| def forward(self, f0: torch.Tensor, upp: int): | |
| """Generates sine waves. | |
| Args: | |
| f0 (torch.Tensor): F0 tensor with shape (batch_size, length, 1). | |
| upp (int): Upsampling factor. | |
| """ | |
| with torch.no_grad(): | |
| f0 = f0[:, None].transpose(1, 2) | |
| f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device) | |
| # fundamental component | |
| f0_buf[:, :, 0] = f0[:, :, 0] | |
| for idx in range(self.harmonic_num): | |
| f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * ( | |
| idx + 2 | |
| ) # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic | |
| rad_values = (f0_buf / float(self.sample_rate)) % 1 | |
| rand_ini = torch.rand( | |
| f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device | |
| ) | |
| rand_ini[:, 0] = 0 | |
| rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini | |
| tmp_over_one = torch.cumsum(rad_values, 1) | |
| tmp_over_one *= upp | |
| tmp_over_one = torch.nn.functional.interpolate( | |
| tmp_over_one.transpose(2, 1), | |
| scale_factor=float(upp), | |
| mode="linear", | |
| align_corners=True, | |
| ).transpose(2, 1) | |
| rad_values = torch.nn.functional.interpolate( | |
| rad_values.transpose(2, 1), scale_factor=float(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 | |
| sine_waves = torch.sin( | |
| torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * torch.pi | |
| ) | |
| sine_waves = sine_waves * self.sine_amp | |
| uv = self._f02uv(f0) | |
| uv = torch.nn.functional.interpolate( | |
| uv.transpose(2, 1), scale_factor=float(upp), mode="nearest" | |
| ).transpose(2, 1) | |
| noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3 | |
| noise = noise_amp * torch.randn_like(sine_waves) | |
| sine_waves = sine_waves * uv + noise | |
| return sine_waves, uv, noise | |