mirror of https://github.com/coqui-ai/TTS.git
refactor(xtts): remove duplicate hifigan generator
This commit is contained in:
Enno Hermann
parent
9035e36b1f
commit
1b6d3ebd33
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@ -1,618 +1,13 @@
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import logging
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import torch
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import torchaudio
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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 trainer.io import load_fsspec
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from TTS.utils.generic_utils import is_pytorch_at_least_2_4
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from TTS.vocoder.models.hifigan_generator import get_padding
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from TTS.encoder.models.resnet import ResNetSpeakerEncoder
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from TTS.vocoder.models.hifigan_generator import HifiganGenerator
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logger = logging.getLogger(__name__)
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LRELU_SLOPE = 0.1
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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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|--------------------------------------------------------------------------------------------------|
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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(
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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=1,
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padding=get_padding(kernel_size, 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=1,
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padding=get_padding(kernel_size, 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=1,
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padding=get_padding(kernel_size, 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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"""
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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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cond_in_each_up_layer=False,
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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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self.cond_in_each_up_layer = cond_in_each_up_layer
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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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if self.cond_in_each_up_layer:
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self.conds = 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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self.conds.append(nn.Conv1d(cond_channels, ch, 1))
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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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if self.cond_in_each_up_layer:
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o = o + self.conds[i](g)
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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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logger.info("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 = torch.load(checkpoint_path, map_location=torch.device("cpu"), weights_only=is_pytorch_at_least_2_4())
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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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class SELayer(nn.Module):
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def __init__(self, channel, reduction=8):
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super(SELayer, self).__init__()
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self.avg_pool = nn.AdaptiveAvgPool2d(1)
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self.fc = nn.Sequential(
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nn.Linear(channel, channel // reduction),
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nn.ReLU(inplace=True),
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nn.Linear(channel // reduction, channel),
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nn.Sigmoid(),
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)
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def forward(self, x):
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b, c, _, _ = x.size()
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y = self.avg_pool(x).view(b, c)
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y = self.fc(y).view(b, c, 1, 1)
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return x * y
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class SEBasicBlock(nn.Module):
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expansion = 1
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def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=8):
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super(SEBasicBlock, self).__init__()
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self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
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self.bn1 = nn.BatchNorm2d(planes)
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self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1, bias=False)
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self.bn2 = nn.BatchNorm2d(planes)
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self.relu = nn.ReLU(inplace=True)
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self.se = SELayer(planes, reduction)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x):
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residual = x
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out = self.conv1(x)
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out = self.relu(out)
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out = self.bn1(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out = self.se(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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out += residual
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out = self.relu(out)
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return out
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def set_init_dict(model_dict, checkpoint_state, c):
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# Partial initialization: if there is a mismatch with new and old layer, it is skipped.
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for k, v in checkpoint_state.items():
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if k not in model_dict:
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logger.warning("Layer missing in the model definition: %s", k)
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# 1. filter out unnecessary keys
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pretrained_dict = {k: v for k, v in checkpoint_state.items() if k in model_dict}
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# 2. filter out different size layers
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pretrained_dict = {k: v for k, v in pretrained_dict.items() if v.numel() == model_dict[k].numel()}
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# 3. skip reinit layers
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if c.has("reinit_layers") and c.reinit_layers is not None:
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for reinit_layer_name in c.reinit_layers:
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pretrained_dict = {k: v for k, v in pretrained_dict.items() if reinit_layer_name not in k}
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# 4. overwrite entries in the existing state dict
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model_dict.update(pretrained_dict)
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logger.info("%d / %d layers are restored.", len(pretrained_dict), len(model_dict))
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return model_dict
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class PreEmphasis(nn.Module):
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def __init__(self, coefficient=0.97):
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super().__init__()
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self.coefficient = coefficient
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self.register_buffer("filter", torch.FloatTensor([-self.coefficient, 1.0]).unsqueeze(0).unsqueeze(0))
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def forward(self, x):
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assert len(x.size()) == 2
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x = torch.nn.functional.pad(x.unsqueeze(1), (1, 0), "reflect")
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return torch.nn.functional.conv1d(x, self.filter).squeeze(1)
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class ResNetSpeakerEncoder(nn.Module):
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"""This is copied from 🐸TTS to remove it from the dependencies."""
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# pylint: disable=W0102
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def __init__(
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self,
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input_dim=64,
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proj_dim=512,
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layers=[3, 4, 6, 3],
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num_filters=[32, 64, 128, 256],
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encoder_type="ASP",
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log_input=False,
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use_torch_spec=False,
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audio_config=None,
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):
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super(ResNetSpeakerEncoder, self).__init__()
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self.encoder_type = encoder_type
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self.input_dim = input_dim
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self.log_input = log_input
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self.use_torch_spec = use_torch_spec
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self.audio_config = audio_config
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self.proj_dim = proj_dim
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self.conv1 = nn.Conv2d(1, num_filters[0], kernel_size=3, stride=1, padding=1)
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self.relu = nn.ReLU(inplace=True)
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self.bn1 = nn.BatchNorm2d(num_filters[0])
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self.inplanes = num_filters[0]
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self.layer1 = self.create_layer(SEBasicBlock, num_filters[0], layers[0])
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self.layer2 = self.create_layer(SEBasicBlock, num_filters[1], layers[1], stride=(2, 2))
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self.layer3 = self.create_layer(SEBasicBlock, num_filters[2], layers[2], stride=(2, 2))
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self.layer4 = self.create_layer(SEBasicBlock, num_filters[3], layers[3], stride=(2, 2))
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self.instancenorm = nn.InstanceNorm1d(input_dim)
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if self.use_torch_spec:
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self.torch_spec = torch.nn.Sequential(
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PreEmphasis(audio_config["preemphasis"]),
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torchaudio.transforms.MelSpectrogram(
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sample_rate=audio_config["sample_rate"],
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n_fft=audio_config["fft_size"],
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win_length=audio_config["win_length"],
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hop_length=audio_config["hop_length"],
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window_fn=torch.hamming_window,
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n_mels=audio_config["num_mels"],
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),
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)
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else:
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self.torch_spec = None
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|
||||
outmap_size = int(self.input_dim / 8)
|
||||
|
||||
self.attention = nn.Sequential(
|
||||
nn.Conv1d(num_filters[3] * outmap_size, 128, kernel_size=1),
|
||||
nn.ReLU(),
|
||||
nn.BatchNorm1d(128),
|
||||
nn.Conv1d(128, num_filters[3] * outmap_size, kernel_size=1),
|
||||
nn.Softmax(dim=2),
|
||||
)
|
||||
|
||||
if self.encoder_type == "SAP":
|
||||
out_dim = num_filters[3] * outmap_size
|
||||
elif self.encoder_type == "ASP":
|
||||
out_dim = num_filters[3] * outmap_size * 2
|
||||
else:
|
||||
raise ValueError("Undefined encoder")
|
||||
|
||||
self.fc = nn.Linear(out_dim, proj_dim)
|
||||
|
||||
self._init_layers()
|
||||
|
||||
def _init_layers(self):
|
||||
for m in self.modules():
|
||||
if isinstance(m, nn.Conv2d):
|
||||
nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
|
||||
elif isinstance(m, nn.BatchNorm2d):
|
||||
nn.init.constant_(m.weight, 1)
|
||||
nn.init.constant_(m.bias, 0)
|
||||
|
||||
def create_layer(self, block, planes, blocks, stride=1):
|
||||
downsample = None
|
||||
if stride != 1 or self.inplanes != planes * block.expansion:
|
||||
downsample = nn.Sequential(
|
||||
nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
|
||||
nn.BatchNorm2d(planes * block.expansion),
|
||||
)
|
||||
|
||||
layers = []
|
||||
layers.append(block(self.inplanes, planes, stride, downsample))
|
||||
self.inplanes = planes * block.expansion
|
||||
for _ in range(1, blocks):
|
||||
layers.append(block(self.inplanes, planes))
|
||||
|
||||
return nn.Sequential(*layers)
|
||||
|
||||
# pylint: disable=R0201
|
||||
def new_parameter(self, *size):
|
||||
out = nn.Parameter(torch.FloatTensor(*size))
|
||||
nn.init.xavier_normal_(out)
|
||||
return out
|
||||
|
||||
def forward(self, x, l2_norm=False):
|
||||
"""Forward pass of the model.
|
||||
|
||||
Args:
|
||||
x (Tensor): Raw waveform signal or spectrogram frames. If input is a waveform, `torch_spec` must be `True`
|
||||
to compute the spectrogram on-the-fly.
|
||||
l2_norm (bool): Whether to L2-normalize the outputs.
|
||||
|
||||
Shapes:
|
||||
- x: :math:`(N, 1, T_{in})` or :math:`(N, D_{spec}, T_{in})`
|
||||
"""
|
||||
x.squeeze_(1)
|
||||
# if you torch spec compute it otherwise use the mel spec computed by the AP
|
||||
if self.use_torch_spec:
|
||||
x = self.torch_spec(x)
|
||||
|
||||
if self.log_input:
|
||||
x = (x + 1e-6).log()
|
||||
x = self.instancenorm(x).unsqueeze(1)
|
||||
|
||||
x = self.conv1(x)
|
||||
x = self.relu(x)
|
||||
x = self.bn1(x)
|
||||
|
||||
x = self.layer1(x)
|
||||
x = self.layer2(x)
|
||||
x = self.layer3(x)
|
||||
x = self.layer4(x)
|
||||
|
||||
x = x.reshape(x.size()[0], -1, x.size()[-1])
|
||||
|
||||
w = self.attention(x)
|
||||
|
||||
if self.encoder_type == "SAP":
|
||||
x = torch.sum(x * w, dim=2)
|
||||
elif self.encoder_type == "ASP":
|
||||
mu = torch.sum(x * w, dim=2)
|
||||
sg = torch.sqrt((torch.sum((x**2) * w, dim=2) - mu**2).clamp(min=1e-5))
|
||||
x = torch.cat((mu, sg), 1)
|
||||
|
||||
x = x.view(x.size()[0], -1)
|
||||
x = self.fc(x)
|
||||
|
||||
if l2_norm:
|
||||
x = torch.nn.functional.normalize(x, p=2, dim=1)
|
||||
return x
|
||||
|
||||
def load_checkpoint(
|
||||
self,
|
||||
checkpoint_path: str,
|
||||
eval: bool = False,
|
||||
use_cuda: bool = False,
|
||||
criterion=None,
|
||||
cache=False,
|
||||
):
|
||||
state = load_fsspec(checkpoint_path, map_location=torch.device("cpu"), cache=cache)
|
||||
try:
|
||||
self.load_state_dict(state["model"])
|
||||
logger.info("Model fully restored.")
|
||||
except (KeyError, RuntimeError) as error:
|
||||
# If eval raise the error
|
||||
if eval:
|
||||
raise error
|
||||
|
||||
logger.info("Partial model initialization.")
|
||||
model_dict = self.state_dict()
|
||||
model_dict = set_init_dict(model_dict, state["model"])
|
||||
self.load_state_dict(model_dict)
|
||||
del model_dict
|
||||
|
||||
# load the criterion for restore_path
|
||||
if criterion is not None and "criterion" in state:
|
||||
try:
|
||||
criterion.load_state_dict(state["criterion"])
|
||||
except (KeyError, RuntimeError) as error:
|
||||
logger.exception("Criterion load ignored because of: %s", error)
|
||||
|
||||
if use_cuda:
|
||||
self.cuda()
|
||||
if criterion is not None:
|
||||
criterion = criterion.cuda()
|
||||
|
||||
if eval:
|
||||
self.eval()
|
||||
assert not self.training
|
||||
|
||||
if not eval:
|
||||
return criterion, state["step"]
|
||||
return criterion
|
||||
|
||||
|
||||
class HifiDecoder(torch.nn.Module):
|
||||
def __init__(
|
||||
|
|
|
|||
|
|
@ -178,6 +178,7 @@ class HifiganGenerator(torch.nn.Module):
|
|||
conv_pre_weight_norm=True,
|
||||
conv_post_weight_norm=True,
|
||||
conv_post_bias=True,
|
||||
cond_in_each_up_layer=False,
|
||||
):
|
||||
r"""HiFiGAN Generator with Multi-Receptive Field Fusion (MRF)
|
||||
|
||||
|
|
@ -202,6 +203,8 @@ class HifiganGenerator(torch.nn.Module):
|
|||
self.inference_padding = inference_padding
|
||||
self.num_kernels = len(resblock_kernel_sizes)
|
||||
self.num_upsamples = len(upsample_factors)
|
||||
self.cond_in_each_up_layer = cond_in_each_up_layer
|
||||
|
||||
# initial upsampling layers
|
||||
self.conv_pre = weight_norm(Conv1d(in_channels, upsample_initial_channel, 7, 1, padding=3))
|
||||
resblock = ResBlock1 if resblock_type == "1" else ResBlock2
|
||||
|
|
@ -236,6 +239,12 @@ class HifiganGenerator(torch.nn.Module):
|
|||
if not conv_post_weight_norm:
|
||||
remove_parametrizations(self.conv_post, "weight")
|
||||
|
||||
if self.cond_in_each_up_layer:
|
||||
self.conds = nn.ModuleList()
|
||||
for i in range(len(self.ups)):
|
||||
ch = upsample_initial_channel // (2 ** (i + 1))
|
||||
self.conds.append(nn.Conv1d(cond_channels, ch, 1))
|
||||
|
||||
def forward(self, x, g=None):
|
||||
"""
|
||||
Args:
|
||||
|
|
@ -255,6 +264,10 @@ class HifiganGenerator(torch.nn.Module):
|
|||
for i in range(self.num_upsamples):
|
||||
o = F.leaky_relu(o, LRELU_SLOPE)
|
||||
o = self.ups[i](o)
|
||||
|
||||
if self.cond_in_each_up_layer:
|
||||
o = o + self.conds[i](g)
|
||||
|
||||
z_sum = None
|
||||
for j in range(self.num_kernels):
|
||||
if z_sum is None:
|
||||
|
|
|
|||
Loading…
Reference in New Issue