mirror of
https://github.com/Nighthawk42/llm-tts-factory.git
synced 2026-08-30 07:22:27 +00:00
62 lines
2.1 KiB
Python
62 lines
2.1 KiB
Python
from typing import Optional
|
|
|
|
import torch
|
|
from torch import nn
|
|
|
|
from .modules import ConvNeXtBlock
|
|
|
|
class VocosBackbone(nn.Module):
|
|
"""
|
|
Vocos backbone module built with ConvNeXt blocks. Supports additional conditioning with Adaptive Layer Normalization
|
|
|
|
Args:
|
|
input_channels (int): Number of input features channels.
|
|
dim (int): Hidden dimension of the model.
|
|
intermediate_dim (int): Intermediate dimension used in ConvNeXtBlock.
|
|
num_layers (int): Number of ConvNeXtBlock layers.
|
|
layer_scale_init_value (float, optional): Initial value for layer scaling. Defaults to `1 / num_layers`.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
input_channels: int,
|
|
dim: int,
|
|
intermediate_dim: int,
|
|
num_layers: int,
|
|
input_kernel_size: int = 9,
|
|
dw_kernel_size: int = 9,
|
|
layer_scale_init_value: Optional[float] = None,
|
|
pad: str = 'zeros',
|
|
):
|
|
super().__init__()
|
|
self.embed = nn.Conv1d(input_channels, dim, kernel_size=input_kernel_size, padding=input_kernel_size//2, padding_mode=pad)
|
|
self.norm = nn.LayerNorm(dim, eps=1e-6)
|
|
self.convnext = nn.ModuleList(
|
|
[
|
|
ConvNeXtBlock(
|
|
dim=dim,
|
|
intermediate_dim=intermediate_dim,
|
|
dw_kernel_size=dw_kernel_size,
|
|
layer_scale_init_value=layer_scale_init_value or 1 / num_layers**0.5,
|
|
)
|
|
for _ in range(num_layers)
|
|
]
|
|
)
|
|
self.final_layer_norm = nn.LayerNorm(dim, eps=1e-6)
|
|
self.apply(self._init_weights)
|
|
|
|
def _init_weights(self, m):
|
|
if isinstance(m, (nn.Conv1d, nn.Linear)):
|
|
nn.init.trunc_normal_(m.weight, std=0.02)
|
|
if m.bias is not None: nn.init.constant_(m.bias, 0)
|
|
|
|
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
|
x = self.embed(x) # (B, C, L)
|
|
x = self.norm(x.transpose(1, 2))
|
|
x = x.transpose(1, 2)
|
|
for conv_block in self.convnext:
|
|
x = conv_block(x)
|
|
x = self.final_layer_norm(x.transpose(1, 2))
|
|
x = x.transpose(1, 2)
|
|
return x
|