coqui-tts/TTS/tts/layers/vits/networks.py

import math

import torch
from torch import nn

from TTS.tts.layers.glow_tts.glow import WN
from TTS.tts.utils.helpers import sequence_mask

LRELU_SLOPE = 0.1


def convert_pad_shape(pad_shape):
    l = pad_shape[::-1]
    pad_shape = [item for sublist in l for item in sublist]
    return pad_shape


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

class ResidualCouplingBlock(nn.Module):
    def __init__(
        self,
        channels,
        hidden_channels,
        kernel_size,
        dilation_rate,
        num_layers,
        dropout_p=0,
        cond_channels=0,
        mean_only=False,
    ):
        assert channels % 2 == 0, "channels should be divisible by 2"
        super().__init__()
        self.half_channels = channels // 2
        self.mean_only = mean_only
        # input layer
        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
        # coupling layers
        self.enc = WN(
            hidden_channels,
            hidden_channels,
            kernel_size,
            dilation_rate,
            num_layers,
            dropout_p=dropout_p,
            c_in_channels=cond_channels,
        )
        # output layer
        # Initializing last layer to 0 makes the affine coupling layers
        # do nothing at first.  This helps with training stability
        self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
        self.post.weight.data.zero_()
        self.post.bias.data.zero_()

    def forward(self, x, x_mask, g=None, reverse=False):
        """
        Note:
            Set `reverse` to True for inference.

        Shapes:
            - x: :math:`[B, C, T]`
            - x_mask: :math:`[B, 1, T]`
            - g: :math:`[B, C, 1]`
        """
        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
        h = self.pre(x0) * x_mask
        h = self.enc(h, x_mask, g=g)
        stats = self.post(h) * x_mask
        if not self.mean_only:
            m, log_scale = torch.split(stats, [self.half_channels] * 2, 1)
        else:
            m = stats
            log_scale = torch.zeros_like(m)

        if not reverse:
            x1 = m + x1 * torch.exp(log_scale) * x_mask
            x = torch.cat([x0, x1], 1)
            logdet = torch.sum(log_scale, [1, 2])
            return x, logdet
        else:
            x1 = (x1 - m) * torch.exp(-log_scale) * x_mask
            x = torch.cat([x0, x1], 1)
            return x


class ResidualCouplingBlocks(nn.Module):
    def __init__(
        self,
        channels: int,
        hidden_channels: int,
        kernel_size: int,
        dilation_rate: int,
        num_layers: int,
        num_flows=4,
        cond_channels=0,
    ):
        """Redisual Coupling blocks for VITS flow layers.

        Args:
            channels (int): Number of input and output tensor channels.
            hidden_channels (int): Number of hidden network channels.
            kernel_size (int): Kernel size of the WaveNet layers.
            dilation_rate (int): Dilation rate of the WaveNet layers.
            num_layers (int): Number of the WaveNet layers.
            num_flows (int, optional): Number of Residual Coupling blocks. Defaults to 4.
            cond_channels (int, optional): Number of channels of the conditioning tensor. Defaults to 0.
        """
        super().__init__()
        self.channels = channels
        self.hidden_channels = hidden_channels
        self.kernel_size = kernel_size
        self.dilation_rate = dilation_rate
        self.num_layers = num_layers
        self.num_flows = num_flows
        self.cond_channels = cond_channels

        self.flows = nn.ModuleList()
        for _ in range(num_flows):
            self.flows.append(
                ResidualCouplingBlock(
                    channels,
                    hidden_channels,
                    kernel_size,
                    dilation_rate,
                    num_layers,
                    cond_channels=cond_channels,
                    mean_only=True,
                )
            )

    def forward(self, x, x_mask, g=None, reverse=False):
        """
        Note:
            Set `reverse` to True for inference.

        Shapes:
            - x: :math:`[B, C, T]`
            - x_mask: :math:`[B, 1, T]`
            - g: :math:`[B, C, 1]`
        """
        if not reverse:
            for flow in self.flows:
                x, _ = flow(x, x_mask, g=g, reverse=reverse)
                x = torch.flip(x, [1])
        else:
            for flow in reversed(self.flows):
                x = torch.flip(x, [1])
                x = flow(x, x_mask, g=g, reverse=reverse)
        return x


class PosteriorEncoder(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        hidden_channels: int,
        kernel_size: int,
        dilation_rate: int,
        num_layers: int,
        cond_channels=0,
    ):
        """Posterior Encoder of VITS model.

        ::
            x -> conv1x1() -> WaveNet() (non-causal) -> conv1x1() -> split() -> [m, s] -> sample(m, s) -> z

        Args:
            in_channels (int): Number of input tensor channels.
            out_channels (int): Number of output tensor channels.
            hidden_channels (int): Number of hidden channels.
            kernel_size (int): Kernel size of the WaveNet convolution layers.
            dilation_rate (int): Dilation rate of the WaveNet layers.
            num_layers (int): Number of the WaveNet layers.
            cond_channels (int, optional): Number of conditioning tensor channels. Defaults to 0.
        """
        super().__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.hidden_channels = hidden_channels
        self.kernel_size = kernel_size
        self.dilation_rate = dilation_rate
        self.num_layers = num_layers
        self.cond_channels = cond_channels

        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
        self.enc = WN(
            hidden_channels, hidden_channels, kernel_size, dilation_rate, num_layers, c_in_channels=cond_channels
        )
        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)

    def forward(self, x, x_lengths, g=None):
        """
        Shapes:
            - x: :math:`[B, C, T]`
            - x_lengths: :math:`[B, 1]`
            - g: :math:`[B, C, 1]`
        """
        x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
        x = self.pre(x) * x_mask
        x = self.enc(x, x_mask, g=g)
        stats = self.proj(x) * x_mask
        mean, log_scale = torch.split(stats, self.out_channels, dim=1)
        z = (mean + torch.randn_like(mean) * torch.exp(log_scale)) * x_mask
        return z, mean, log_scale, x_mask