Modules/Vocoder/AMP.py

# Copyright (c) 2022 NVIDIA CORPORATION.
#   Licensed under the MIT license.

# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
#   LICENSE is in incl_licenses directory.


from alias_free_torch import *
from alias_free_torch import Activation1d
from torch.nn import Conv1d
from torch.nn.utils import remove_weight_norm
from torch.nn.utils import weight_norm

from Modules.Vocoder.Snake import SnakeBeta

LRELU_SLOPE = 0.1


class AMPBlock1(torch.nn.Module):
    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
        super(AMPBlock1, self).__init__()

        self.convs1 = nn.ModuleList([
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
                               padding=get_padding(kernel_size, dilation[0]))),
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
                               padding=get_padding(kernel_size, dilation[1]))),
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
                               padding=get_padding(kernel_size, dilation[2])))
        ])
        self.convs1.apply(init_weights)

        self.convs2 = nn.ModuleList([
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
                               padding=get_padding(kernel_size, 1))),
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
                               padding=get_padding(kernel_size, 1))),
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
                               padding=get_padding(kernel_size, 1)))
        ])
        self.convs2.apply(init_weights)

        self.num_layers = len(self.convs1) + len(self.convs2)  # total number of conv layers

        self.activations = nn.ModuleList([
            Activation1d(
                activation=SnakeBeta(channels, alpha_logscale=True))
            for _ in range(self.num_layers)
        ])

    def forward(self, x):
        acts1, acts2 = self.activations[::2], self.activations[1::2]
        for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2):
            xt = a1(x)
            xt = c1(xt)
            xt = a2(xt)
            xt = c2(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)


def init_weights(m, mean=0.0, std=0.01):
    classname = m.__class__.__name__
    if classname.find("Conv") != -1:
        m.weight.data.normal_(mean, std)


def apply_weight_norm(m):
    classname = m.__class__.__name__
    if classname.find("Conv") != -1:
        weight_norm(m)


def get_padding(kernel_size, dilation=1):
    return int((kernel_size * dilation - dilation) / 2)