mirror of
https://github.com/Nighthawk42/llm-tts-factory.git
synced 2026-08-30 07:22:27 +00:00
48 lines
1.6 KiB
Python
48 lines
1.6 KiB
Python
import torch
|
|
from torch import nn
|
|
|
|
|
|
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.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
dim: int,
|
|
intermediate_dim: int,
|
|
layer_scale_init_value: float,
|
|
dw_kernel_size: int = 9,
|
|
):
|
|
super().__init__()
|
|
self.dwconv = nn.Conv1d(dim, dim, kernel_size=dw_kernel_size, padding=dw_kernel_size//2, groups=dim) # depthwise conv
|
|
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) -> torch.Tensor:
|
|
residual = x
|
|
x = self.dwconv(x)
|
|
x = x.transpose(1, 2) # (B, C, T) -> (B, T, C)
|
|
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
|