models.py

import copy
import math

import torch
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm

import attentions
import commons
import modules
import monotonic_align
from commons import get_padding, init_weights

AVAILABLE_FLOW_TYPES = [
    "pre_conv",
    "pre_conv2",
    "fft",
    "mono_layer_inter_residual",
    "mono_layer_post_residual",
]

AVAILABLE_DURATION_DISCRIMINATOR_TYPES = [
    "dur_disc_1",
    "dur_disc_2",
]


class StochasticDurationPredictor(nn.Module):
    def __init__(
        self,
        in_channels,
        filter_channels,
        kernel_size,
        p_dropout,
        n_flows=4,
        gin_channels=0,
    ):
        super().__init__()
        filter_channels = in_channels  # it needs to be removed from future version.
        self.in_channels = in_channels
        self.filter_channels = filter_channels
        self.kernel_size = kernel_size
        self.p_dropout = p_dropout
        self.n_flows = n_flows
        self.gin_channels = gin_channels

        self.log_flow = modules.Log()
        self.flows = nn.ModuleList()
        self.flows.append(modules.ElementwiseAffine(2))
        for i in range(n_flows):
            self.flows.append(
                modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
            )
            self.flows.append(modules.Flip())

        self.post_pre = nn.Conv1d(1, filter_channels, 1)
        self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
        self.post_convs = modules.DDSConv(
            filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
        )
        self.post_flows = nn.ModuleList()
        self.post_flows.append(modules.ElementwiseAffine(2))
        for i in range(4):
            self.post_flows.append(
                modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
            )
            self.post_flows.append(modules.Flip())

        self.pre = nn.Conv1d(in_channels, filter_channels, 1)
        self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
        self.convs = modules.DDSConv(
            filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
        )
        if gin_channels != 0:
            self.cond = nn.Conv1d(gin_channels, filter_channels, 1)

    def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
        x = torch.detach(x)
        x = self.pre(x)
        if g is not None:
            g = torch.detach(g)
            x = x + self.cond(g)
        x = self.convs(x, x_mask)
        x = self.proj(x) * x_mask

        if not reverse:
            flows = self.flows
            assert w is not None

            logdet_tot_q = 0
            h_w = self.post_pre(w)
            h_w = self.post_convs(h_w, x_mask)
            h_w = self.post_proj(h_w) * x_mask
            e_q = (
                torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype)
                * x_mask
            )
            z_q = e_q
            for flow in self.post_flows:
                z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
                logdet_tot_q += logdet_q
            z_u, z1 = torch.split(z_q, [1, 1], 1)
            u = torch.sigmoid(z_u) * x_mask
            z0 = (w - u) * x_mask
            logdet_tot_q += torch.sum(
                (F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2]
            )
            logq = (
                torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q**2)) * x_mask, [1, 2])
                - logdet_tot_q
            )

            logdet_tot = 0
            z0, logdet = self.log_flow(z0, x_mask)
            logdet_tot += logdet
            z = torch.cat([z0, z1], 1)
            for flow in flows:
                z, logdet = flow(z, x_mask, g=x, reverse=reverse)
                logdet_tot = logdet_tot + logdet
            nll = (
                torch.sum(0.5 * (math.log(2 * math.pi) + (z**2)) * x_mask, [1, 2])
                - logdet_tot
            )
            return nll + logq  # [b]
        else:
            flows = list(reversed(self.flows))
            flows = flows[:-2] + [flows[-1]]  # remove a useless vflow
            z = (
                torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype)
                * noise_scale
            )
            for flow in flows:
                z = flow(z, x_mask, g=x, reverse=reverse)
            z0, z1 = torch.split(z, [1, 1], 1)
            logw = z0
            return logw


class DurationPredictor(nn.Module):
    def __init__(
        self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
    ):
        super().__init__()

        self.in_channels = in_channels
        self.filter_channels = filter_channels
        self.kernel_size = kernel_size
        self.p_dropout = p_dropout
        self.gin_channels = gin_channels

        self.drop = nn.Dropout(p_dropout)
        self.conv_1 = nn.Conv1d(
            in_channels, filter_channels, kernel_size, padding=kernel_size // 2
        )
        self.norm_1 = modules.LayerNorm(filter_channels)
        self.conv_2 = nn.Conv1d(
            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
        )
        self.norm_2 = modules.LayerNorm(filter_channels)
        self.proj = nn.Conv1d(filter_channels, 1, 1)

        if gin_channels != 0:
            self.cond = nn.Conv1d(gin_channels, in_channels, 1)

    def forward(self, x, x_mask, g=None):
        x = torch.detach(x)
        if g is not None:
            g = torch.detach(g)
            x = x + self.cond(g)
        x = self.conv_1(x * x_mask)
        x = torch.relu(x)
        x = self.norm_1(x)
        x = self.drop(x)
        x = self.conv_2(x * x_mask)
        x = torch.relu(x)
        x = self.norm_2(x)
        x = self.drop(x)
        x = self.proj(x * x_mask)
        return x * x_mask


class DurationDiscriminatorV1(nn.Module):  # vits2
    # TODO : not using "spk conditioning" for now according to the paper.
    # Can be a better discriminator if we use it.
    def __init__(
        self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
    ):
        super().__init__()

        self.in_channels = in_channels
        self.filter_channels = filter_channels
        self.kernel_size = kernel_size
        self.p_dropout = p_dropout
        self.gin_channels = gin_channels

        self.drop = nn.Dropout(p_dropout)
        self.conv_1 = nn.Conv1d(
            in_channels, filter_channels, kernel_size, padding=kernel_size // 2
        )
        # self.norm_1 = modules.LayerNorm(filter_channels)
        self.conv_2 = nn.Conv1d(
            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
        )
        # self.norm_2 = modules.LayerNorm(filter_channels)
        self.dur_proj = nn.Conv1d(1, filter_channels, 1)

        self.pre_out_conv_1 = nn.Conv1d(
            2 * filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
        )
        self.pre_out_norm_1 = modules.LayerNorm(filter_channels)
        self.pre_out_conv_2 = nn.Conv1d(
            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
        )
        self.pre_out_norm_2 = modules.LayerNorm(filter_channels)

        # if gin_channels != 0:
        #   self.cond = nn.Conv1d(gin_channels, in_channels, 1)

        self.output_layer = nn.Sequential(nn.Linear(filter_channels, 1), nn.Sigmoid())

    def forward_probability(self, x, x_mask, dur, g=None):
        dur = self.dur_proj(dur)
        x = torch.cat([x, dur], dim=1)
        x = self.pre_out_conv_1(x * x_mask)
        # x = torch.relu(x)
        # x = self.pre_out_norm_1(x)
        # x = self.drop(x)
        x = self.pre_out_conv_2(x * x_mask)
        # x = torch.relu(x)
        # x = self.pre_out_norm_2(x)
        # x = self.drop(x)
        x = x * x_mask
        x = x.transpose(1, 2)
        output_prob = self.output_layer(x)
        return output_prob

    def forward(self, x, x_mask, dur_r, dur_hat, g=None):
        x = torch.detach(x)
        # if g is not None:
        #   g = torch.detach(g)
        #   x = x + self.cond(g)
        x = self.conv_1(x * x_mask)
        # x = torch.relu(x)
        # x = self.norm_1(x)
        # x = self.drop(x)
        x = self.conv_2(x * x_mask)
        # x = torch.relu(x)
        # x = self.norm_2(x)
        # x = self.drop(x)

        output_probs = []
        for dur in [dur_r, dur_hat]:
            output_prob = self.forward_probability(x, x_mask, dur, g)
            output_probs.append(output_prob)

        return output_probs


class DurationDiscriminatorV2(nn.Module):  # vits2
    # TODO : not using "spk conditioning" for now according to the paper.
    # Can be a better discriminator if we use it.
    def __init__(
        self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
    ):
        super().__init__()

        self.in_channels = in_channels
        self.filter_channels = filter_channels
        self.kernel_size = kernel_size
        self.p_dropout = p_dropout
        self.gin_channels = gin_channels

        self.conv_1 = nn.Conv1d(
            in_channels, filter_channels, kernel_size, padding=kernel_size // 2
        )
        self.norm_1 = modules.LayerNorm(filter_channels)
        self.conv_2 = nn.Conv1d(
            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
        )
        self.norm_2 = modules.LayerNorm(filter_channels)
        self.dur_proj = nn.Conv1d(1, filter_channels, 1)

        self.pre_out_conv_1 = nn.Conv1d(
            2 * filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
        )
        self.pre_out_norm_1 = modules.LayerNorm(filter_channels)
        self.pre_out_conv_2 = nn.Conv1d(
            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
        )
        self.pre_out_norm_2 = modules.LayerNorm(filter_channels)

        # if gin_channels != 0:
        #   self.cond = nn.Conv1d(gin_channels, in_channels, 1)

        self.output_layer = nn.Sequential(nn.Linear(filter_channels, 1), nn.Sigmoid())

    def forward_probability(self, x, x_mask, dur, g=None):
        dur = self.dur_proj(dur)
        x = torch.cat([x, dur], dim=1)
        x = self.pre_out_conv_1(x * x_mask)
        x = torch.relu(x)
        x = self.pre_out_norm_1(x)
        x = self.pre_out_conv_2(x * x_mask)
        x = torch.relu(x)
        x = self.pre_out_norm_2(x)
        x = x * x_mask
        x = x.transpose(1, 2)
        output_prob = self.output_layer(x)
        return output_prob

    def forward(self, x, x_mask, dur_r, dur_hat, g=None):
        x = torch.detach(x)
        # if g is not None:
        #   g = torch.detach(g)
        #   x = x + self.cond(g)
        x = self.conv_1(x * x_mask)
        x = torch.relu(x)
        x = self.norm_1(x)
        x = self.conv_2(x * x_mask)
        x = torch.relu(x)
        x = self.norm_2(x)

        output_probs = []
        for dur in [dur_r, dur_hat]:
            output_prob = self.forward_probability(x, x_mask, dur, g)
            output_probs.append([output_prob])

        return output_probs


class TextEncoder(nn.Module):
    def __init__(
        self,
        n_vocab,
        out_channels,
        hidden_channels,
        filter_channels,
        n_heads,
        n_layers,
        kernel_size,
        p_dropout,
        gin_channels=0,
    ):
        super().__init__()
        self.n_vocab = n_vocab
        self.out_channels = out_channels
        self.hidden_channels = hidden_channels
        self.filter_channels = filter_channels
        self.n_heads = n_heads
        self.n_layers = n_layers
        self.kernel_size = kernel_size
        self.p_dropout = p_dropout
        self.gin_channels = gin_channels
        self.emb = nn.Embedding(n_vocab, hidden_channels)
        nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)

        self.encoder = attentions.Encoder(
            hidden_channels,
            filter_channels,
            n_heads,
            n_layers,
            kernel_size,
            p_dropout,
            gin_channels=self.gin_channels,
        )
        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)

    def forward(self, x, x_lengths, g=None):
        x = self.emb(x) * math.sqrt(self.hidden_channels)  # [b, t, h]
        x = torch.transpose(x, 1, -1)  # [b, h, t]
        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
            x.dtype
        )

        x = self.encoder(x * x_mask, x_mask, g=g)
        stats = self.proj(x) * x_mask

        m, logs = torch.split(stats, self.out_channels, dim=1)
        return x, m, logs, x_mask


class ResidualCouplingTransformersLayer2(nn.Module):  # vits2
    def __init__(
        self,
        channels,
        hidden_channels,
        kernel_size,
        dilation_rate,
        n_layers,
        p_dropout=0,
        gin_channels=0,
        mean_only=False,
    ):
        assert channels % 2 == 0, "channels should be divisible by 2"
        super().__init__()
        self.channels = channels
        self.hidden_channels = hidden_channels
        self.kernel_size = kernel_size
        self.dilation_rate = dilation_rate
        self.n_layers = n_layers
        self.half_channels = channels // 2
        self.mean_only = mean_only

        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
        self.pre_transformer = attentions.Encoder(
            hidden_channels,
            hidden_channels,
            n_heads=2,
            n_layers=1,
            kernel_size=kernel_size,
            p_dropout=p_dropout,
            # window_size=None,
        )
        self.enc = modules.WN(
            hidden_channels,
            kernel_size,
            dilation_rate,
            n_layers,
            p_dropout=p_dropout,
            gin_channels=gin_channels,
        )

        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):
        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
        h = self.pre(x0) * x_mask
        h = h + self.pre_transformer(h * x_mask, x_mask)  # vits2 residual connection
        h = self.enc(h, x_mask, g=g)
        stats = self.post(h) * x_mask
        if not self.mean_only:
            m, logs = torch.split(stats, [self.half_channels] * 2, 1)
        else:
            m = stats
            logs = torch.zeros_like(m)
        if not reverse:
            x1 = m + x1 * torch.exp(logs) * x_mask
            x = torch.cat([x0, x1], 1)
            logdet = torch.sum(logs, [1, 2])
            return x, logdet
        else:
            x1 = (x1 - m) * torch.exp(-logs) * x_mask
            x = torch.cat([x0, x1], 1)
            return x


class ResidualCouplingTransformersLayer(nn.Module):  # vits2
    def __init__(
        self,
        channels,
        hidden_channels,
        kernel_size,
        dilation_rate,
        n_layers,
        p_dropout=0,
        gin_channels=0,
        mean_only=False,
    ):
        assert channels % 2 == 0, "channels should be divisible by 2"
        super().__init__()
        self.channels = channels
        self.hidden_channels = hidden_channels
        self.kernel_size = kernel_size
        self.dilation_rate = dilation_rate
        self.n_layers = n_layers
        self.half_channels = channels // 2
        self.mean_only = mean_only
        # vits2
        self.pre_transformer = attentions.Encoder(
            self.half_channels,
            self.half_channels,
            n_heads=2,
            n_layers=2,
            kernel_size=3,
            p_dropout=0.1,
            window_size=None,
        )

        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
        self.enc = modules.WN(
            hidden_channels,
            kernel_size,
            dilation_rate,
            n_layers,
            p_dropout=p_dropout,
            gin_channels=gin_channels,
        )
        # vits2
        self.post_transformer = attentions.Encoder(
            self.hidden_channels,
            self.hidden_channels,
            n_heads=2,
            n_layers=2,
            kernel_size=3,
            p_dropout=0.1,
            window_size=None,
        )

        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):
        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
        x0_ = self.pre_transformer(x0 * x_mask, x_mask)  # vits2
        x0_ = x0_ + x0  # vits2 residual connection
        h = self.pre(x0_) * x_mask  # changed from x0 to x0_ to retain x0 for the flow
        h = self.enc(h, x_mask, g=g)

        # vits2 - (experimental;uncomment the following 2 line to use)
        # h_ = self.post_transformer(h, x_mask)
        # h = h + h_ #vits2 residual connection

        stats = self.post(h) * x_mask
        if not self.mean_only:
            m, logs = torch.split(stats, [self.half_channels] * 2, 1)
        else:
            m = stats
            logs = torch.zeros_like(m)
        if not reverse:
            x1 = m + x1 * torch.exp(logs) * x_mask
            x = torch.cat([x0, x1], 1)
            logdet = torch.sum(logs, [1, 2])
            return x, logdet
        else:
            x1 = (x1 - m) * torch.exp(-logs) * x_mask
            x = torch.cat([x0, x1], 1)
            return x


class FFTransformerCouplingLayer(nn.Module):  # vits2
    def __init__(
        self,
        channels,
        hidden_channels,
        kernel_size,
        n_layers,
        n_heads,
        p_dropout=0,
        filter_channels=768,
        mean_only=False,
        gin_channels=0,
    ):
        assert channels % 2 == 0, "channels should be divisible by 2"
        super().__init__()
        self.channels = channels
        self.hidden_channels = hidden_channels
        self.kernel_size = kernel_size
        self.n_layers = n_layers
        self.half_channels = channels // 2
        self.mean_only = mean_only

        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
        self.enc = attentions.FFT(
            hidden_channels,
            filter_channels,
            n_heads,
            n_layers,
            kernel_size,
            p_dropout,
            isflow=True,
            gin_channels=gin_channels,
        )
        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):
        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
        h = self.pre(x0) * x_mask
        h_ = self.enc(h, x_mask, g=g)
        h = h_ + h
        stats = self.post(h) * x_mask
        if not self.mean_only:
            m, logs = torch.split(stats, [self.half_channels] * 2, 1)
        else:
            m = stats
            logs = torch.zeros_like(m)

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


class MonoTransformerFlowLayer(nn.Module):  # vits2
    def __init__(
        self,
        channels,
        hidden_channels,
        mean_only=False,
        residual_connection=False,
        # according to VITS-2 paper fig 1B set residual_connection=True
    ):
        assert channels % 2 == 0, "channels should be divisible by 2"
        super().__init__()
        self.channels = channels
        self.hidden_channels = hidden_channels
        self.half_channels = channels // 2
        self.mean_only = mean_only
        self.residual_connection = residual_connection
        # vits2
        self.pre_transformer = attentions.Encoder(
            self.half_channels,
            self.half_channels,
            n_heads=2,
            n_layers=2,
            kernel_size=3,
            p_dropout=0.1,
            window_size=None,
        )

        self.post = nn.Conv1d(
            self.half_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):
        if self.residual_connection:
            if not reverse:
                x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
                x0_ = self.pre_transformer(x0, x_mask)  # vits2
                stats = self.post(x0_) * x_mask
                if not self.mean_only:
                    m, logs = torch.split(stats, [self.half_channels] * 2, 1)
                else:
                    m = stats
                    logs = torch.zeros_like(m)
                x1 = m + x1 * torch.exp(logs) * x_mask
                x_ = torch.cat([x0, x1], 1)
                x = x + x_
                logdet = torch.sum(torch.log(torch.exp(logs) + 1), [1, 2])
                logdet = logdet + torch.log(torch.tensor(2)) * (
                    x0.shape[1] * x0.shape[2]
                )
                return x, logdet
            else:
                x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
                x0 = x0 / 2
                x0_ = x0 * x_mask
                x0_ = self.pre_transformer(x0, x_mask)  # vits2
                stats = self.post(x0_) * x_mask
                if not self.mean_only:
                    m, logs = torch.split(stats, [self.half_channels] * 2, 1)
                else:
                    m = stats
                    logs = torch.zeros_like(m)
                x1_ = ((x1 - m) / (1 + torch.exp(-logs))) * x_mask
                x = torch.cat([x0, x1_], 1)
                return x
        else:
            x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
            x0_ = self.pre_transformer(x0 * x_mask, x_mask)  # vits2
            h = x0_ + x0  # vits2
            stats = self.post(h) * x_mask
            if not self.mean_only:
                m, logs = torch.split(stats, [self.half_channels] * 2, 1)
            else:
                m = stats
                logs = torch.zeros_like(m)
            if not reverse:
                x1 = m + x1 * torch.exp(logs) * x_mask
                x = torch.cat([x0, x1], 1)
                logdet = torch.sum(logs, [1, 2])
                return x, logdet
            else:
                x1 = (x1 - m) * torch.exp(-logs) * x_mask
                x = torch.cat([x0, x1], 1)
                return x


class ResidualCouplingTransformersBlock(nn.Module):  # vits2
    def __init__(
        self,
        channels,
        hidden_channels,
        kernel_size,
        dilation_rate,
        n_layers,
        n_flows=4,
        gin_channels=0,
        use_transformer_flows=False,
        transformer_flow_type="pre_conv",
    ):
        super().__init__()
        self.channels = channels
        self.hidden_channels = hidden_channels
        self.kernel_size = kernel_size
        self.dilation_rate = dilation_rate
        self.n_layers = n_layers
        self.n_flows = n_flows
        self.gin_channels = gin_channels

        self.flows = nn.ModuleList()
        if use_transformer_flows:
            if transformer_flow_type == "pre_conv":
                for i in range(n_flows):
                    self.flows.append(
                        ResidualCouplingTransformersLayer(
                            channels,
                            hidden_channels,
                            kernel_size,
                            dilation_rate,
                            n_layers,
                            gin_channels=gin_channels,
                            mean_only=True,
                        )
                    )
                    self.flows.append(modules.Flip())
            elif transformer_flow_type == "pre_conv2":
                for i in range(n_flows):
                    self.flows.append(
                        ResidualCouplingTransformersLayer2(
                            channels,
                            hidden_channels,
                            kernel_size,
                            dilation_rate,
                            n_layers,
                            gin_channels=gin_channels,
                            mean_only=True,
                        )
                    )
                    self.flows.append(modules.Flip())
            elif transformer_flow_type == "fft":
                for i in range(n_flows):
                    self.flows.append(
                        FFTransformerCouplingLayer(
                            channels,
                            hidden_channels,
                            kernel_size,
                            dilation_rate,
                            n_layers,
                            gin_channels=gin_channels,
                            mean_only=True,
                        )
                    )
                    self.flows.append(modules.Flip())
            elif transformer_flow_type == "mono_layer_inter_residual":
                for i in range(n_flows):
                    self.flows.append(
                        modules.ResidualCouplingLayer(
                            channels,
                            hidden_channels,
                            kernel_size,
                            dilation_rate,
                            n_layers,
                            gin_channels=gin_channels,
                            mean_only=True,
                        )
                    )
                    self.flows.append(modules.Flip())
                    self.flows.append(
                        MonoTransformerFlowLayer(
                            channels, hidden_channels, mean_only=True
                        )
                    )
            elif transformer_flow_type == "mono_layer_post_residual":
                for i in range(n_flows):
                    self.flows.append(
                        modules.ResidualCouplingLayer(
                            channels,
                            hidden_channels,
                            kernel_size,
                            dilation_rate,
                            n_layers,
                            gin_channels=gin_channels,
                            mean_only=True,
                        )
                    )
                    self.flows.append(modules.Flip())
                    self.flows.append(
                        MonoTransformerFlowLayer(
                            channels,
                            hidden_channels,
                            mean_only=True,
                            residual_connection=True,
                        )
                    )
        else:
            for i in range(n_flows):
                self.flows.append(
                    modules.ResidualCouplingLayer(
                        channels,
                        hidden_channels,
                        kernel_size,
                        dilation_rate,
                        n_layers,
                        gin_channels=gin_channels,
                        mean_only=True,
                    )
                )
                self.flows.append(modules.Flip())

    def forward(self, x, x_mask, g=None, reverse=False):
        if not reverse:
            for flow in self.flows:
                x, _ = flow(x, x_mask, g=g, reverse=reverse)
        else:
            for flow in reversed(self.flows):
                x = flow(x, x_mask, g=g, reverse=reverse)
        return x


class ResidualCouplingBlock(nn.Module):
    def __init__(
        self,
        channels,
        hidden_channels,
        kernel_size,
        dilation_rate,
        n_layers,
        n_flows=4,
        gin_channels=0,
    ):
        super().__init__()
        self.channels = channels
        self.hidden_channels = hidden_channels
        self.kernel_size = kernel_size
        self.dilation_rate = dilation_rate
        self.n_layers = n_layers
        self.n_flows = n_flows
        self.gin_channels = gin_channels

        self.flows = nn.ModuleList()
        for i in range(n_flows):
            self.flows.append(
                modules.ResidualCouplingLayer(
                    channels,
                    hidden_channels,
                    kernel_size,
                    dilation_rate,
                    n_layers,
                    gin_channels=gin_channels,
                    mean_only=True,
                )
            )
            self.flows.append(modules.Flip())

    def forward(self, x, x_mask, g=None, reverse=False):
        if not reverse:
            for flow in self.flows:
                x, _ = flow(x, x_mask, g=g, reverse=reverse)
        else:
            for flow in reversed(self.flows):
                x = flow(x, x_mask, g=g, reverse=reverse)
        return x


class PosteriorEncoder(nn.Module):
    def __init__(
        self,
        in_channels,
        out_channels,
        hidden_channels,
        kernel_size,
        dilation_rate,
        n_layers,
        gin_channels=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.n_layers = n_layers
        self.gin_channels = gin_channels

        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
        self.enc = modules.WN(
            hidden_channels,
            kernel_size,
            dilation_rate,
            n_layers,
            gin_channels=gin_channels,
        )
        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)

    def forward(self, x, x_lengths, g=None):
        x_mask = torch.unsqueeze(commons.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
        m, logs = torch.split(stats, self.out_channels, dim=1)
        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
        return z, m, logs, x_mask


class Generator(torch.nn.Module):
    def __init__(
        self,
        initial_channel,
        resblock,
        resblock_kernel_sizes,
        resblock_dilation_sizes,
        upsample_rates,
        upsample_initial_channel,
        upsample_kernel_sizes,
        gin_channels=0,
    ):
        super(Generator, self).__init__()
        self.num_kernels = len(resblock_kernel_sizes)
        self.num_upsamples = len(upsample_rates)
        self.conv_pre = Conv1d(
            initial_channel, upsample_initial_channel, 7, 1, padding=3
        )
        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2

        self.ups = nn.ModuleList()
        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
            self.ups.append(
                weight_norm(
                    ConvTranspose1d(
                        upsample_initial_channel // (2**i),
                        upsample_initial_channel // (2 ** (i + 1)),
                        k,
                        u,
                        padding=(k - u) // 2,
                    )
                )
            )

        self.resblocks = nn.ModuleList()
        for i in range(len(self.ups)):
            ch = upsample_initial_channel // (2 ** (i + 1))
            for j, (k, d) in enumerate(
                zip(resblock_kernel_sizes, resblock_dilation_sizes)
            ):
                self.resblocks.append(resblock(ch, k, d))

        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
        self.ups.apply(init_weights)

        if gin_channels != 0:
            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)

    def forward(self, x, g=None):
        x = self.conv_pre(x)
        if g is not None:
            x = x + self.cond(g)

        for i in range(self.num_upsamples):
            x = F.leaky_relu(x, modules.LRELU_SLOPE)
            x = self.ups[i](x)
            xs = None
            for j in range(self.num_kernels):
                if xs is None:
                    xs = self.resblocks[i * self.num_kernels + j](x)
                else:
                    xs += self.resblocks[i * self.num_kernels + j](x)
            x = xs / self.num_kernels
        x = F.leaky_relu(x)
        x = self.conv_post(x)
        x = torch.tanh(x)

        return x

    def remove_weight_norm(self):
        print("Removing weight norm...")
        for l in self.ups:
            remove_weight_norm(l)
        for l in self.resblocks:
            l.remove_weight_norm()


class DiscriminatorP(torch.nn.Module):
    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
        super(DiscriminatorP, self).__init__()
        self.period = period
        self.use_spectral_norm = use_spectral_norm
        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
        self.convs = nn.ModuleList(
            [
                norm_f(
                    Conv2d(
                        1,
                        32,
                        (kernel_size, 1),
                        (stride, 1),
                        padding=(get_padding(kernel_size, 1), 0),
                    )
                ),
                norm_f(
                    Conv2d(
                        32,
                        128,
                        (kernel_size, 1),
                        (stride, 1),
                        padding=(get_padding(kernel_size, 1), 0),
                    )
                ),
                norm_f(
                    Conv2d(
                        128,
                        512,
                        (kernel_size, 1),
                        (stride, 1),
                        padding=(get_padding(kernel_size, 1), 0),
                    )
                ),
                norm_f(
                    Conv2d(
                        512,
                        1024,
                        (kernel_size, 1),
                        (stride, 1),
                        padding=(get_padding(kernel_size, 1), 0),
                    )
                ),
                norm_f(
                    Conv2d(
                        1024,
                        1024,
                        (kernel_size, 1),
                        1,
                        padding=(get_padding(kernel_size, 1), 0),
                    )
                ),
            ]
        )
        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))

    def forward(self, x):
        fmap = []

        # 1d to 2d
        b, c, t = x.shape
        if t % self.period != 0:  # pad first
            n_pad = self.period - (t % self.period)
            x = F.pad(x, (0, n_pad), "reflect")
            t = t + n_pad
        x = x.view(b, c, t // self.period, self.period)

        for l in self.convs:
            x = l(x)
            x = F.leaky_relu(x, modules.LRELU_SLOPE)
            fmap.append(x)
        x = self.conv_post(x)
        fmap.append(x)
        x = torch.flatten(x, 1, -1)

        return x, fmap


class DiscriminatorS(torch.nn.Module):
    def __init__(self, use_spectral_norm=False):
        super(DiscriminatorS, self).__init__()
        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
        self.convs = nn.ModuleList(
            [
                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
            ]
        )
        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))

    def forward(self, x):
        fmap = []

        for l in self.convs:
            x = l(x)
            x = F.leaky_relu(x, modules.LRELU_SLOPE)
            fmap.append(x)
        x = self.conv_post(x)
        fmap.append(x)
        x = torch.flatten(x, 1, -1)

        return x, fmap


class MultiPeriodDiscriminator(torch.nn.Module):
    def __init__(self, use_spectral_norm=False):
        super(MultiPeriodDiscriminator, self).__init__()
        periods = [2, 3, 5, 7, 11]

        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
        discs = discs + [
            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
        ]
        self.discriminators = nn.ModuleList(discs)

    def forward(self, y, y_hat):
        y_d_rs = []
        y_d_gs = []
        fmap_rs = []
        fmap_gs = []
        for i, d in enumerate(self.discriminators):
            y_d_r, fmap_r = d(y)
            y_d_g, fmap_g = d(y_hat)
            y_d_rs.append(y_d_r)
            y_d_gs.append(y_d_g)
            fmap_rs.append(fmap_r)
            fmap_gs.append(fmap_g)

        return y_d_rs, y_d_gs, fmap_rs, fmap_gs


class SynthesizerTrn(nn.Module):
    """
    Synthesizer for Training
    """

    def __init__(
        self,
        n_vocab,
        spec_channels,
        segment_size,
        inter_channels,
        hidden_channels,
        filter_channels,
        n_heads,
        n_layers,
        kernel_size,
        p_dropout,
        resblock,
        resblock_kernel_sizes,
        resblock_dilation_sizes,
        upsample_rates,
        upsample_initial_channel,
        upsample_kernel_sizes,
        n_speakers=0,
        gin_channels=0,
        use_sdp=True,
        **kwargs,
    ):
        super().__init__()
        self.n_vocab = n_vocab
        self.spec_channels = spec_channels
        self.inter_channels = inter_channels
        self.hidden_channels = hidden_channels
        self.filter_channels = filter_channels
        self.n_heads = n_heads
        self.n_layers = n_layers
        self.kernel_size = kernel_size
        self.p_dropout = p_dropout
        self.resblock = resblock
        self.resblock_kernel_sizes = resblock_kernel_sizes
        self.resblock_dilation_sizes = resblock_dilation_sizes
        self.upsample_rates = upsample_rates
        self.upsample_initial_channel = upsample_initial_channel
        self.upsample_kernel_sizes = upsample_kernel_sizes
        self.segment_size = segment_size
        self.n_speakers = n_speakers
        self.gin_channels = gin_channels
        self.use_spk_conditioned_encoder = kwargs.get(
            "use_spk_conditioned_encoder", False
        )
        self.use_transformer_flows = kwargs.get("use_transformer_flows", False)
        self.transformer_flow_type = kwargs.get(
            "transformer_flow_type", "mono_layer_post_residual"
        )
        if self.use_transformer_flows:
            assert (
                self.transformer_flow_type in AVAILABLE_FLOW_TYPES
            ), f"transformer_flow_type must be one of {AVAILABLE_FLOW_TYPES}"
        self.use_sdp = use_sdp
        # self.use_duration_discriminator = kwargs.get("use_duration_discriminator", False)
        self.use_noise_scaled_mas = kwargs.get("use_noise_scaled_mas", False)
        self.mas_noise_scale_initial = kwargs.get("mas_noise_scale_initial", 0.01)
        self.noise_scale_delta = kwargs.get("noise_scale_delta", 2e-6)

        self.current_mas_noise_scale = self.mas_noise_scale_initial
        if self.use_spk_conditioned_encoder and gin_channels > 0:
            self.enc_gin_channels = gin_channels
        else:
            self.enc_gin_channels = 0
        self.enc_p = TextEncoder(
            n_vocab,
            inter_channels,
            hidden_channels,
            filter_channels,
            n_heads,
            n_layers,
            kernel_size,
            p_dropout,
            gin_channels=self.enc_gin_channels,
        )

        self.dec = Generator(
            inter_channels,
            resblock,
            resblock_kernel_sizes,
            resblock_dilation_sizes,
            upsample_rates,
            upsample_initial_channel,
            upsample_kernel_sizes,
            gin_channels=gin_channels,
        )
        self.enc_q = PosteriorEncoder(
            spec_channels,
            inter_channels,
            hidden_channels,
            5,
            1,
            16,
            gin_channels=gin_channels,
        )
        # self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
        self.flow = ResidualCouplingTransformersBlock(
            inter_channels,
            hidden_channels,
            5,
            1,
            4,
            gin_channels=gin_channels,
            use_transformer_flows=self.use_transformer_flows,
            transformer_flow_type=self.transformer_flow_type,
        )

        if use_sdp:
            self.dp = StochasticDurationPredictor(
                hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels
            )
        else:
            self.dp = DurationPredictor(
                hidden_channels, 256, 3, 0.5, gin_channels=gin_channels
            )

        if n_speakers > 1:
            self.emb_g = nn.Embedding(n_speakers, gin_channels)

    def forward(self, x, x_lengths, y, y_lengths, sid=None):
        if self.n_speakers > 0:
            g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
        else:
            g = None

        x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, g=g)
        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
        z_p = self.flow(z, y_mask, g=g)

        with torch.no_grad():
            # negative cross-entropy
            s_p_sq_r = torch.exp(-2 * logs_p)  # [b, d, t]
            neg_cent1 = torch.sum(
                -0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True
            )  # [b, 1, t_s]
            neg_cent2 = torch.matmul(
                -0.5 * (z_p**2).transpose(1, 2), s_p_sq_r
            )  # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
            neg_cent3 = torch.matmul(
                z_p.transpose(1, 2), (m_p * s_p_sq_r)
            )  # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
            neg_cent4 = torch.sum(
                -0.5 * (m_p**2) * s_p_sq_r, [1], keepdim=True
            )  # [b, 1, t_s]
            neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4

            if self.use_noise_scaled_mas:
                epsilon = (
                    torch.std(neg_cent)
                    * torch.randn_like(neg_cent)
                    * self.current_mas_noise_scale
                )
                neg_cent = neg_cent + epsilon

            attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
            attn = (
                monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1))
                .unsqueeze(1)
                .detach()
            )

        w = attn.sum(2)
        if self.use_sdp:
            l_length = self.dp(x, x_mask, w, g=g)
            l_length = l_length / torch.sum(x_mask)
            logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=1.0)
            logw_ = torch.log(w + 1e-6) * x_mask
        else:
            logw_ = torch.log(w + 1e-6) * x_mask
            logw = self.dp(x, x_mask, g=g)
            l_length = torch.sum((logw - logw_) ** 2, [1, 2]) / torch.sum(
                x_mask
            )  # for averaging

        # expand prior
        m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
        logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)

        z_slice, ids_slice = commons.rand_slice_segments(
            z, y_lengths, self.segment_size
        )
        o = self.dec(z_slice, g=g)
        return (
            o,
            l_length,
            attn,
            ids_slice,
            x_mask,
            y_mask,
            (z, z_p, m_p, logs_p, m_q, logs_q),
            (x, logw, logw_),
        )

    def infer(
        self,
        x,
        x_lengths,
        sid=None,
        noise_scale=1,
        length_scale=1,
        noise_scale_w=1.0,
        max_len=None,
    ):
        if self.n_speakers > 0:
            g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
        else:
            g = None
        x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, g=g)
        if self.use_sdp:
            logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
        else:
            logw = self.dp(x, x_mask, g=g)
        w = torch.exp(logw) * x_mask * length_scale
        w_ceil = torch.ceil(w)
        y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
        y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(
            x_mask.dtype
        )
        attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
        attn = commons.generate_path(w_ceil, attn_mask)

        m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(
            1, 2
        )  # [b, t', t], [b, t, d] -> [b, d, t']
        logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(
            1, 2
        )  # [b, t', t], [b, t, d] -> [b, d, t']

        z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
        z = self.flow(z_p, y_mask, g=g, reverse=True)
        o = self.dec((z * y_mask)[:, :, :max_len], g=g)
        return o, attn, y_mask, (z, z_p, m_p, logs_p)

    # currently vits-2 is not capable of voice conversion
    ## comment - choihkk
    ## Assuming the use of the ResidualCouplingTransformersLayer2 module, it seems that voice conversion is possible 
    def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
        assert self.n_speakers > 0, "n_speakers have to be larger than 0."
        g_src = self.emb_g(sid_src).unsqueeze(-1)
        g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
        z_p = self.flow(z, y_mask, g=g_src)
        z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
        o_hat = self.dec(z_hat * y_mask, g=g_tgt)
        return o_hat, y_mask, (z, z_p, z_hat)