from typing import Optional, Tuple import torch from torch import nn from torch.nn.utils import weight_norm, remove_weight_norm class ConvNeXtBlock(nn.Module): """ConvNeXt Block adapted from https://github.com/facebookresearch/ConvNeXt to 1D audio signal. Args: dim (int): Number of input channels. intermediate_dim (int): Dimensionality of the intermediate layer. layer_scale_init_value (float, optional): Initial value for the layer scale. None means no scaling. Defaults to None. adanorm_num_embeddings (int, optional): Number of embeddings for AdaLayerNorm. None means non-conditional LayerNorm. Defaults to None. """ def __init__( self, dim: int, intermediate_dim: int, layer_scale_init_value: float, adanorm_num_embeddings: Optional[int] = None, ): super().__init__() self.dwconv = nn.Conv1d(dim, dim, kernel_size=7, padding=3, groups=dim) # depthwise conv self.adanorm = adanorm_num_embeddings is not None if adanorm_num_embeddings: self.norm = AdaLayerNorm(adanorm_num_embeddings, dim, eps=1e-6) else: self.norm = nn.LayerNorm(dim, eps=1e-6) self.pwconv1 = nn.Linear(dim, intermediate_dim) # pointwise/1x1 convs, implemented with linear layers self.act = nn.GELU() self.pwconv2 = nn.Linear(intermediate_dim, dim) self.gamma = ( nn.Parameter(layer_scale_init_value * torch.ones(dim), requires_grad=True) if layer_scale_init_value > 0 else None ) def forward(self, x: torch.Tensor, cond_embedding_id: Optional[torch.Tensor] = None) -> torch.Tensor: residual = x x = self.dwconv(x) x = x.transpose(1, 2) # (B, C, T) -> (B, T, C) if self.adanorm: assert cond_embedding_id is not None x = self.norm(x, cond_embedding_id) else: x = self.norm(x) x = self.pwconv1(x) x = self.act(x) x = self.pwconv2(x) if self.gamma is not None: x = self.gamma * x x = x.transpose(1, 2) # (B, T, C) -> (B, C, T) x = residual + x return x class AdaLayerNorm(nn.Module): """ Adaptive Layer Normalization module with learnable embeddings per `num_embeddings` classes Args: num_embeddings (int): Number of embeddings. embedding_dim (int): Dimension of the embeddings. """ def __init__(self, num_embeddings: int, embedding_dim: int, eps: float = 1e-6): super().__init__() self.eps = eps self.dim = embedding_dim self.scale = nn.Embedding(num_embeddings=num_embeddings, embedding_dim=embedding_dim) self.shift = nn.Embedding(num_embeddings=num_embeddings, embedding_dim=embedding_dim) torch.nn.init.ones_(self.scale.weight) torch.nn.init.zeros_(self.shift.weight) def forward(self, x: torch.Tensor, cond_embedding_id: torch.Tensor) -> torch.Tensor: scale = self.scale(cond_embedding_id) shift = self.shift(cond_embedding_id) x = nn.functional.layer_norm(x, (self.dim,), eps=self.eps) x = x * scale + shift return x class ResBlock1(nn.Module): """ ResBlock adapted from HiFi-GAN V1 (https://github.com/jik876/hifi-gan) with dilated 1D convolutions, but without upsampling layers. Args: dim (int): Number of input channels. kernel_size (int, optional): Size of the convolutional kernel. Defaults to 3. dilation (tuple[int], optional): Dilation factors for the dilated convolutions. Defaults to (1, 3, 5). lrelu_slope (float, optional): Negative slope of the LeakyReLU activation function. Defaults to 0.1. layer_scale_init_value (float, optional): Initial value for the layer scale. None means no scaling. Defaults to None. """ def __init__( self, dim: int, kernel_size: int = 3, dilation: Tuple[int, int, int] = (1, 3, 5), lrelu_slope: float = 0.1, layer_scale_init_value: Optional[float] = None, ): super().__init__() self.lrelu_slope = lrelu_slope self.convs1 = nn.ModuleList( [ weight_norm( nn.Conv1d( dim, dim, kernel_size, 1, dilation=dilation[0], padding=self.get_padding(kernel_size, dilation[0]), ) ), weight_norm( nn.Conv1d( dim, dim, kernel_size, 1, dilation=dilation[1], padding=self.get_padding(kernel_size, dilation[1]), ) ), weight_norm( nn.Conv1d( dim, dim, kernel_size, 1, dilation=dilation[2], padding=self.get_padding(kernel_size, dilation[2]), ) ), ] ) self.convs2 = nn.ModuleList( [ weight_norm(nn.Conv1d(dim, dim, kernel_size, 1, dilation=1, padding=self.get_padding(kernel_size, 1))), weight_norm(nn.Conv1d(dim, dim, kernel_size, 1, dilation=1, padding=self.get_padding(kernel_size, 1))), weight_norm(nn.Conv1d(dim, dim, kernel_size, 1, dilation=1, padding=self.get_padding(kernel_size, 1))), ] ) self.gamma = nn.ParameterList( [ nn.Parameter(layer_scale_init_value * torch.ones(dim, 1), requires_grad=True) if layer_scale_init_value is not None else None, nn.Parameter(layer_scale_init_value * torch.ones(dim, 1), requires_grad=True) if layer_scale_init_value is not None else None, nn.Parameter(layer_scale_init_value * torch.ones(dim, 1), requires_grad=True) if layer_scale_init_value is not None else None, ] ) def forward(self, x: torch.Tensor) -> torch.Tensor: for c1, c2, gamma in zip(self.convs1, self.convs2, self.gamma): xt = torch.nn.functional.leaky_relu(x, negative_slope=self.lrelu_slope) xt = c1(xt) xt = torch.nn.functional.leaky_relu(xt, negative_slope=self.lrelu_slope) xt = c2(xt) if gamma is not None: xt = gamma * xt x = xt + x return x def remove_weight_norm(self): for l in self.convs1: remove_weight_norm(l) for l in self.convs2: remove_weight_norm(l) @staticmethod def get_padding(kernel_size: int, dilation: int = 1) -> int: return int((kernel_size * dilation - dilation) / 2) def safe_log(x: torch.Tensor, clip_val: float = 1e-7) -> torch.Tensor: """ Computes the element-wise logarithm of the input tensor with clipping to avoid near-zero values. Args: x (Tensor): Input tensor. clip_val (float, optional): Minimum value to clip the input tensor. Defaults to 1e-7. Returns: Tensor: Element-wise logarithm of the input tensor with clipping applied. """ return torch.log(torch.clip(x, min=clip_val)) def symlog(x: torch.Tensor) -> torch.Tensor: return torch.sign(x) * torch.log1p(x.abs()) def symexp(x: torch.Tensor) -> torch.Tensor: return torch.sign(x) * (torch.exp(x.abs()) - 1)