302 lines
10 KiB
Python
302 lines
10 KiB
Python
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# adopted from https://github.com/jik876/hifi-gan/blob/master/models.py
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import torch
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from torch import nn
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from torch.nn import Conv1d, ConvTranspose1d
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from torch.nn import functional as F
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from torch.nn.utils.parametrizations import weight_norm
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from torch.nn.utils.parametrize import remove_parametrizations
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from TTS.utils.io import load_fsspec
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LRELU_SLOPE = 0.1
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def get_padding(k, d):
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return int((k * d - d) / 2)
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class ResBlock1(torch.nn.Module):
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"""Residual Block Type 1. It has 3 convolutional layers in each convolutional block.
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Network::
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x -> lrelu -> conv1_1 -> conv1_2 -> conv1_3 -> z -> lrelu -> conv2_1 -> conv2_2 -> conv2_3 -> o -> + -> o
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Args:
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channels (int): number of hidden channels for the convolutional layers.
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kernel_size (int): size of the convolution filter in each layer.
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dilations (list): list of dilation value for each conv layer in a block.
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"""
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def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
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super().__init__()
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self.convs1 = nn.ModuleList(
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[
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weight_norm(
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Conv1d(
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channels,
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channels,
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kernel_size,
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1,
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dilation=dilation[0],
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padding=get_padding(kernel_size, dilation[0]),
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)
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),
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weight_norm(
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Conv1d(
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channels,
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channels,
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kernel_size,
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1,
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dilation=dilation[1],
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padding=get_padding(kernel_size, dilation[1]),
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)
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),
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weight_norm(
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Conv1d(
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channels,
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channels,
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kernel_size,
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1,
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dilation=dilation[2],
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padding=get_padding(kernel_size, dilation[2]),
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)
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),
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]
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)
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self.convs2 = nn.ModuleList(
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[
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weight_norm(
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Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))
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),
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weight_norm(
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Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))
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),
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weight_norm(
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Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))
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),
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]
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)
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def forward(self, x):
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"""
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Args:
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x (Tensor): input tensor.
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Returns:
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Tensor: output tensor.
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Shapes:
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x: [B, C, T]
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"""
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for c1, c2 in zip(self.convs1, self.convs2):
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xt = F.leaky_relu(x, LRELU_SLOPE)
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xt = c1(xt)
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xt = F.leaky_relu(xt, LRELU_SLOPE)
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xt = c2(xt)
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x = xt + x
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return x
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def remove_weight_norm(self):
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for l in self.convs1:
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remove_parametrizations(l, "weight")
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for l in self.convs2:
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remove_parametrizations(l, "weight")
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class ResBlock2(torch.nn.Module):
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"""Residual Block Type 2. It has 1 convolutional layers in each convolutional block.
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Network::
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x -> lrelu -> conv1-> -> z -> lrelu -> conv2-> o -> + -> o
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|---------------------------------------------------|
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Args:
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channels (int): number of hidden channels for the convolutional layers.
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kernel_size (int): size of the convolution filter in each layer.
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dilations (list): list of dilation value for each conv layer in a block.
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"""
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def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
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super().__init__()
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self.convs = nn.ModuleList(
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[
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weight_norm(
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Conv1d(
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channels,
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channels,
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kernel_size,
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1,
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dilation=dilation[0],
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padding=get_padding(kernel_size, dilation[0]),
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)
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),
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weight_norm(
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Conv1d(
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channels,
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channels,
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kernel_size,
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1,
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dilation=dilation[1],
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padding=get_padding(kernel_size, dilation[1]),
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)
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),
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]
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)
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def forward(self, x):
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for c in self.convs:
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xt = F.leaky_relu(x, LRELU_SLOPE)
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xt = c(xt)
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x = xt + x
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return x
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def remove_weight_norm(self):
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for l in self.convs:
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remove_parametrizations(l, "weight")
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class HifiganGenerator(torch.nn.Module):
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def __init__(
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self,
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in_channels,
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out_channels,
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resblock_type,
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resblock_dilation_sizes,
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resblock_kernel_sizes,
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upsample_kernel_sizes,
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upsample_initial_channel,
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upsample_factors,
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inference_padding=5,
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cond_channels=0,
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conv_pre_weight_norm=True,
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conv_post_weight_norm=True,
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conv_post_bias=True,
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):
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r"""HiFiGAN Generator with Multi-Receptive Field Fusion (MRF)
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Network:
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x -> lrelu -> upsampling_layer -> resblock1_k1x1 -> z1 -> + -> z_sum / #resblocks -> lrelu -> conv_post_7x1 -> tanh -> o
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.. -> zI ---|
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resblockN_kNx1 -> zN ---'
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Args:
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in_channels (int): number of input tensor channels.
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out_channels (int): number of output tensor channels.
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resblock_type (str): type of the `ResBlock`. '1' or '2'.
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resblock_dilation_sizes (List[List[int]]): list of dilation values in each layer of a `ResBlock`.
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resblock_kernel_sizes (List[int]): list of kernel sizes for each `ResBlock`.
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upsample_kernel_sizes (List[int]): list of kernel sizes for each transposed convolution.
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upsample_initial_channel (int): number of channels for the first upsampling layer. This is divided by 2
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for each consecutive upsampling layer.
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upsample_factors (List[int]): upsampling factors (stride) for each upsampling layer.
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inference_padding (int): constant padding applied to the input at inference time. Defaults to 5.
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"""
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super().__init__()
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self.inference_padding = inference_padding
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self.num_kernels = len(resblock_kernel_sizes)
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self.num_upsamples = len(upsample_factors)
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# initial upsampling layers
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self.conv_pre = weight_norm(Conv1d(in_channels, upsample_initial_channel, 7, 1, padding=3))
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resblock = ResBlock1 if resblock_type == "1" else ResBlock2
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# upsampling layers
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self.ups = nn.ModuleList()
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for i, (u, k) in enumerate(zip(upsample_factors, upsample_kernel_sizes)):
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self.ups.append(
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weight_norm(
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ConvTranspose1d(
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upsample_initial_channel // (2**i),
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upsample_initial_channel // (2 ** (i + 1)),
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k,
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u,
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padding=(k - u) // 2,
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)
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)
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)
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# MRF blocks
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self.resblocks = nn.ModuleList()
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for i in range(len(self.ups)):
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ch = upsample_initial_channel // (2 ** (i + 1))
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for _, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
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self.resblocks.append(resblock(ch, k, d))
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# post convolution layer
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self.conv_post = weight_norm(Conv1d(ch, out_channels, 7, 1, padding=3, bias=conv_post_bias))
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if cond_channels > 0:
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self.cond_layer = nn.Conv1d(cond_channels, upsample_initial_channel, 1)
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if not conv_pre_weight_norm:
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remove_parametrizations(self.conv_pre, "weight")
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if not conv_post_weight_norm:
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remove_parametrizations(self.conv_post, "weight")
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def forward(self, x, g=None):
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"""
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Args:
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x (Tensor): feature input tensor.
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g (Tensor): global conditioning input tensor.
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Returns:
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Tensor: output waveform.
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Shapes:
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x: [B, C, T]
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Tensor: [B, 1, T]
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"""
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o = self.conv_pre(x)
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if hasattr(self, "cond_layer"):
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o = o + self.cond_layer(g)
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for i in range(self.num_upsamples):
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o = F.leaky_relu(o, LRELU_SLOPE)
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o = self.ups[i](o)
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z_sum = None
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for j in range(self.num_kernels):
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if z_sum is None:
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z_sum = self.resblocks[i * self.num_kernels + j](o)
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else:
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z_sum += self.resblocks[i * self.num_kernels + j](o)
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o = z_sum / self.num_kernels
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o = F.leaky_relu(o)
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o = self.conv_post(o)
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o = torch.tanh(o)
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return o
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@torch.no_grad()
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def inference(self, c):
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"""
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Args:
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x (Tensor): conditioning input tensor.
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Returns:
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Tensor: output waveform.
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Shapes:
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x: [B, C, T]
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Tensor: [B, 1, T]
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"""
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c = c.to(self.conv_pre.weight.device)
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c = torch.nn.functional.pad(c, (self.inference_padding, self.inference_padding), "replicate")
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return self.forward(c)
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def remove_weight_norm(self):
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print("Removing weight norm...")
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for l in self.ups:
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remove_parametrizations(l, "weight")
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for l in self.resblocks:
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l.remove_weight_norm()
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remove_parametrizations(self.conv_pre, "weight")
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remove_parametrizations(self.conv_post, "weight")
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def load_checkpoint(
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self, config, checkpoint_path, eval=False, cache=False
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): # pylint: disable=unused-argument, redefined-builtin
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state = load_fsspec(checkpoint_path, map_location=torch.device("cpu"), cache=cache)
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self.load_state_dict(state["model"])
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if eval:
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self.eval()
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assert not self.training
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self.remove_weight_norm()
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