neucodec/tconv/t_first.py

import torch
from torch import nn

from ..distill_layers import Conv1d
from . import base


class V3FirstBlock(base.FirstBlock):  # (1, 5, 11, 21, 45)
    def __init__(
        self,
        target_dim,
        conv_kernels=(7, 7, 7, 7, 7),
        pool_kernels=(1, 5, 11, 21, 45),
        dilation_rate=7,
    ):
        h_dim = len(pool_kernels) * 4
        super().__init__(h_dim, conv_kernels, pool_kernels, dilation_rate=dilation_rate)
        self.conv_1 = Conv1d(h_dim, h_dim * 4, kernel_size=1)
        self.act = nn.GELU()
        self.conv_2 = Conv1d(h_dim * 4 + 1, target_dim, kernel_size=1)

    def forward(self, x):
        h = super().forward(x)
        h = self.conv_1(h)
        h = self.act(h)
        y = torch.cat([h, x], dim=1)
        y = self.conv_2(y)
        return y


FirstBlock = lambda dim: (
    V3FirstBlock(
        dim,
        conv_kernels=(7, 7, 7, 7, 7),
        pool_kernels=(1, 5, 11, 21, 45),
        dilation_rate=99,
    )  # fv36
)