Update models.py

models.py

@@ -1,3 +1,4 @@
+import copy
 import math
 import torch
 from torch import nn
@@ -8,10 +9,16 @@ import modules
 import attentions
 import monotonic_align
 
-from torch.nn import Conv1d, ConvTranspose1d, Conv2d
+from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
 from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
 from commons import init_weights, get_padding
 
+from pqmf import PQMF
+from stft import TorchSTFT, OnnxSTFT
+
+AVAILABLE_FLOW_TYPES = ["pre_conv", "pre_conv2", "fft", "mono_layer_inter_residual", "mono_layer_post_residual"]
+AVAILABLE_DURATION_DISCRIMINATOR_TYPES = {"dur_disc_1": "DurationDiscriminator", "dur_disc_2": "DurationDiscriminator2"}
+
 
 class StochasticDurationPredictor(nn.Module):
     def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
@@ -131,6 +138,148 @@ class DurationPredictor(nn.Module):
         return x * x_mask
 
 
+class DurationDiscriminator(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 DurationDiscriminator2(nn.Module):  # vits2 - DurationDiscriminator2
+    # 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,
@@ -140,7 +289,8 @@ class TextEncoder(nn.Module):
                  n_heads,
                  n_layers,
                  kernel_size,
-                 p_dropout):
+                 p_dropout,
+                 gin_channels=0):
         super().__init__()
         self.n_vocab = n_vocab
         self.out_channels = out_channels
@@ -150,10 +300,9 @@ class TextEncoder(nn.Module):
         self.n_layers = n_layers
         self.kernel_size = kernel_size
         self.p_dropout = p_dropout
-
-        if self.n_vocab != 0:
-            self.emb = nn.Embedding(n_vocab, hidden_channels)
-            nn.init.normal_(self.emb.weight, 0.0, hidden_channels ** -0.5)
+        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,
@@ -161,22 +310,455 @@ class TextEncoder(nn.Module):
             n_heads,
             n_layers,
             kernel_size,
-            p_dropout)
+            p_dropout,
+            gin_channels=self.gin_channels)
         self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
 
-    def forward(self, x, x_lengths):
-        if self.n_vocab != 0:
-            x = self.emb(x) * math.sqrt(self.hidden_channels)  # [b, t, h]
+    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)
+        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
+
+    def remove_weight_norm(self): # !
+        self.enc.remove_weight_norm()
+
+
+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_ = 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 = 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()
+        # TODO : clean up this mess
+        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
+
+    def remove_weight_norm(self): # !
+        for i, l in enumerate(self.flows):
+            if i % 2 == 0:
+                l.remove_weight_norm()
+
+
 class ResidualCouplingBlock(nn.Module):
     def __init__(self,
                  channels,
@@ -198,8 +780,16 @@ class ResidualCouplingBlock(nn.Module):
         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))
+                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):
@@ -211,6 +801,11 @@ class ResidualCouplingBlock(nn.Module):
                 x = flow(x, x_mask, g=g, reverse=reverse)
         return x
 
+    def remove_weight_norm(self): # !
+        for i, l in enumerate(self.flows):
+            if i % 2 == 0:
+                l.remove_weight_norm()
+
 
 class PosteriorEncoder(nn.Module):
     def __init__(self,
@@ -300,6 +895,272 @@ class Generator(torch.nn.Module):
             l.remove_weight_norm()
 
 
+class iSTFT_Generator(torch.nn.Module):
+    def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates,
+                 upsample_initial_channel, upsample_kernel_sizes, gen_istft_n_fft, gen_istft_hop_size,
+                 gin_channels=0, is_onnx=False):
+        super(iSTFT_Generator, self).__init__()
+        # self.h = h
+        self.gen_istft_n_fft = gen_istft_n_fft
+        self.gen_istft_hop_size = gen_istft_hop_size
+
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        self.conv_pre = weight_norm(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.post_n_fft = self.gen_istft_n_fft
+        self.conv_post = weight_norm(Conv1d(ch, self.post_n_fft + 2, 7, 1, padding=3))
+        self.ups.apply(init_weights)
+        self.conv_post.apply(init_weights)
+        self.reflection_pad = torch.nn.ReflectionPad1d((1, 0))
+        '''
+        self.stft = TorchSTFT(filter_length=self.gen_istft_n_fft, hop_length=self.gen_istft_hop_size,
+                              win_length=self.gen_istft_n_fft)
+        '''
+        # - for onnx
+        if is_onnx == True:
+            self.stft = OnnxSTFT(filter_length=self.gen_istft_n_fft, hop_length=self.gen_istft_hop_size,
+                                 win_length=self.gen_istft_n_fft)
+        else:
+            self.stft = TorchSTFT(filter_length=self.gen_istft_n_fft, hop_length=self.gen_istft_hop_size,
+                                  win_length=self.gen_istft_n_fft)
+
+    def forward(self, x, g=None):
+        x = self.conv_pre(x)
+        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.reflection_pad(x)
+        x = self.conv_post(x)
+        spec = torch.exp(x[:, :self.post_n_fft // 2 + 1, :])
+        phase = math.pi * torch.sin(x[:, self.post_n_fft // 2 + 1:, :])
+        out = self.stft.inverse(spec, phase).to(x.device)
+        return out, None
+
+    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()
+        remove_weight_norm(self.conv_pre)
+        remove_weight_norm(self.conv_post)
+
+
+class Multiband_iSTFT_Generator(torch.nn.Module): # !
+    def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates,
+                 upsample_initial_channel, upsample_kernel_sizes, gen_istft_n_fft, gen_istft_hop_size, subbands,
+                 gin_channels=0, is_onnx=False):
+        super(Multiband_iSTFT_Generator, self).__init__()
+        # self.h = h
+        self.subbands = subbands
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        self.conv_pre = weight_norm(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.post_n_fft = gen_istft_n_fft
+        self.ups.apply(init_weights)
+        self.reflection_pad = torch.nn.ReflectionPad1d((1, 0))
+        self.reshape_pixelshuffle = []
+
+        self.subband_conv_post = weight_norm(Conv1d(ch, self.subbands * (self.post_n_fft + 2), 7, 1, padding=3))
+
+        self.subband_conv_post.apply(init_weights)
+
+        self.gen_istft_n_fft = gen_istft_n_fft
+        self.gen_istft_hop_size = gen_istft_hop_size
+
+        #- for onnx
+        if is_onnx == True:
+            self.stft = OnnxSTFT(filter_length=self.gen_istft_n_fft, hop_length=self.gen_istft_hop_size, win_length=self.gen_istft_n_fft)
+        else:
+            self.stft = TorchSTFT(filter_length=self.gen_istft_n_fft, hop_length=self.gen_istft_hop_size, win_length=self.gen_istft_n_fft)
+
+    def forward(self, x, g=None):
+        '''
+        stft = TorchSTFT(filter_length=self.gen_istft_n_fft, hop_length=self.gen_istft_hop_size,
+                         win_length=self.gen_istft_n_fft).to(x.device) # !
+        '''
+        stft = self.stft.to(x.device)
+        pqmf = PQMF(x.device)
+
+        x = self.conv_pre(x)  # [B, ch, length]
+
+        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.reflection_pad(x)
+        x = self.subband_conv_post(x)
+        x = torch.reshape(x, (x.shape[0], self.subbands, x.shape[1] // self.subbands, x.shape[-1]))
+
+        spec = torch.exp(x[:, :, :self.post_n_fft // 2 + 1, :])
+        phase = math.pi * torch.sin(x[:, :, self.post_n_fft // 2 + 1:, :])
+
+        y_mb_hat = stft.inverse(
+            torch.reshape(spec, (spec.shape[0] * self.subbands, self.gen_istft_n_fft // 2 + 1, spec.shape[-1])),
+            torch.reshape(phase, (phase.shape[0] * self.subbands, self.gen_istft_n_fft // 2 + 1, phase.shape[-1])))
+        y_mb_hat = torch.reshape(y_mb_hat, (x.shape[0], self.subbands, 1, y_mb_hat.shape[-1]))
+        y_mb_hat = y_mb_hat.squeeze(-2)
+
+        y_g_hat = pqmf.synthesis(y_mb_hat)
+
+        return y_g_hat, y_mb_hat
+
+    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 Multistream_iSTFT_Generator(torch.nn.Module):
+    def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates,
+                 upsample_initial_channel, upsample_kernel_sizes, gen_istft_n_fft, gen_istft_hop_size, subbands,
+                 gin_channels=0, is_onnx=False):
+        super(Multistream_iSTFT_Generator, self).__init__()
+        # self.h = h
+        self.subbands = subbands
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        self.conv_pre = weight_norm(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.post_n_fft = gen_istft_n_fft
+        self.ups.apply(init_weights)
+        self.reflection_pad = torch.nn.ReflectionPad1d((1, 0))
+        self.reshape_pixelshuffle = []
+
+        self.subband_conv_post = weight_norm(Conv1d(ch, self.subbands * (self.post_n_fft + 2), 7, 1, padding=3))
+
+        self.subband_conv_post.apply(init_weights)
+
+        self.gen_istft_n_fft = gen_istft_n_fft
+        self.gen_istft_hop_size = gen_istft_hop_size
+
+        updown_filter = torch.zeros((self.subbands, self.subbands, self.subbands)).float()
+        for k in range(self.subbands):
+            updown_filter[k, k, 0] = 1.0
+        self.register_buffer("updown_filter", updown_filter)
+        #self.multistream_conv_post = weight_norm(Conv1d(4, 1, kernel_size=63, bias=False, padding=get_padding(63, 1)))
+        self.multistream_conv_post = weight_norm(Conv1d(self.subbands, 1, kernel_size=63, bias=False, padding=get_padding(63, 1))) # from MB-iSTFT-VITS-44100-Ja
+        self.multistream_conv_post.apply(init_weights)
+
+        #- for onnx
+        if is_onnx == True:
+            self.stft = OnnxSTFT(filter_length=self.gen_istft_n_fft, hop_length=self.gen_istft_hop_size, win_length=self.gen_istft_n_fft)
+        else:
+            self.stft = TorchSTFT(filter_length=self.gen_istft_n_fft, hop_length=self.gen_istft_hop_size, win_length=self.gen_istft_n_fft)
+
+    def forward(self, x, g=None):
+        '''
+        stft = TorchSTFT(filter_length=self.gen_istft_n_fft, hop_length=self.gen_istft_hop_size,
+                         win_length=self.gen_istft_n_fft).to(x.device) # !
+        '''
+        stft = self.stft.to(x.device)
+
+        # pqmf = PQMF(x.device)
+
+        x = self.conv_pre(x)  # [B, ch, length]
+
+        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.reflection_pad(x)
+        x = self.subband_conv_post(x)
+        x = torch.reshape(x, (x.shape[0], self.subbands, x.shape[1] // self.subbands, x.shape[-1]))
+
+        spec = torch.exp(x[:, :, :self.post_n_fft // 2 + 1, :])
+        phase = math.pi * torch.sin(x[:, :, self.post_n_fft // 2 + 1:, :])
+
+        y_mb_hat = stft.inverse(
+            torch.reshape(spec, (spec.shape[0] * self.subbands, self.gen_istft_n_fft // 2 + 1, spec.shape[-1])),
+            torch.reshape(phase, (phase.shape[0] * self.subbands, self.gen_istft_n_fft // 2 + 1, phase.shape[-1])))
+        y_mb_hat = torch.reshape(y_mb_hat, (x.shape[0], self.subbands, 1, y_mb_hat.shape[-1]))
+        y_mb_hat = y_mb_hat.squeeze(-2)
+
+        #y_mb_hat = F.conv_transpose1d(y_mb_hat, self.updown_filter.cuda(x.device) * self.subbands, stride=self.subbands)
+        y_mb_hat = F.conv_transpose1d(y_mb_hat, self.updown_filter.to(x.device) * self.subbands, stride=self.subbands)
+
+        y_g_hat = self.multistream_conv_post(y_mb_hat)
+
+        return y_g_hat, y_mb_hat
+
+    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__()
@@ -412,9 +1273,16 @@ class SynthesizerTrn(nn.Module):
                  upsample_rates,
                  upsample_initial_channel,
                  upsample_kernel_sizes,
+                 gen_istft_n_fft,
+                 gen_istft_hop_size,
                  n_speakers=0,
                  gin_channels=0,
                  use_sdp=True,
+                 ms_istft_vits=False,
+                 mb_istft_vits=False,
+                 subbands=False,
+                 istft_vits=False,
+                 is_onnx=False,
                  **kwargs):
 
         super().__init__()
@@ -436,9 +1304,25 @@ class SynthesizerTrn(nn.Module):
         self.segment_size = segment_size
         self.n_speakers = n_speakers
         self.gin_channels = gin_channels
-
+        self.ms_istft_vits = ms_istft_vits
+        self.mb_istft_vits = mb_istft_vits
+        self.istft_vits = istft_vits
+        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,
@@ -446,29 +1330,60 @@ class SynthesizerTrn(nn.Module):
                                  n_heads,
                                  n_layers,
                                  kernel_size,
-                                 p_dropout)
-        self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates,
-                             upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
+                                 p_dropout,
+                                 gin_channels=self.enc_gin_channels)
+
+        if mb_istft_vits == True:
+            print('Multi-band iSTFT VITS2')
+            self.dec = Multiband_iSTFT_Generator(inter_channels, resblock, resblock_kernel_sizes,
+                                                 resblock_dilation_sizes,
+                                                 upsample_rates, upsample_initial_channel, upsample_kernel_sizes,
+                                                 gen_istft_n_fft, gen_istft_hop_size, subbands,
+                                                 gin_channels=gin_channels, is_onnx=is_onnx)
+        elif ms_istft_vits == True:
+            print('Multi-stream iSTFT VITS2')
+            self.dec = Multistream_iSTFT_Generator(inter_channels, resblock, resblock_kernel_sizes,
+                                                   resblock_dilation_sizes,
+                                                   upsample_rates, upsample_initial_channel, upsample_kernel_sizes,
+                                                   gen_istft_n_fft, gen_istft_hop_size, subbands,
+                                                   gin_channels=gin_channels, is_onnx=is_onnx)
+        elif istft_vits == True:
+            print('iSTFT-VITS2')
+            self.dec = iSTFT_Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes,
+                                       upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gen_istft_n_fft,
+                                       gen_istft_hop_size, gin_channels=gin_channels, is_onnx=is_onnx)
+        else:
+            print('No iSTFT arguments found in json file')
+            self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes,
+                                 upsample_rates,
+                                 upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)  # vits 2
+
         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 = 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)
+        self.emb_g = nn.Embedding(n_speakers, gin_channels)
 
     def forward(self, x, x_lengths, y, y_lengths, sid=None):
+        # x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
+        g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
 
-        x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
-        if self.n_speakers > 1:
-            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)  # vits2?
         z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
         z_p = self.flow(z, y_mask, g=g)
 
@@ -482,6 +1397,10 @@ class SynthesizerTrn(nn.Module):
             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()
 
@@ -489,6 +1408,8 @@ class SynthesizerTrn(nn.Module):
         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.)
+            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)
@@ -499,16 +1420,13 @@ class SynthesizerTrn(nn.Module):
         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)
+        o, o_mb = self.dec(z_slice, g=g)
+        return o, o_mb, 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., max_len=None):
-        x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
-        if self.n_speakers > 1:
-            g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
-        else:
-            g = None
+        g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
 
+        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:
@@ -526,15 +1444,21 @@ class SynthesizerTrn(nn.Module):
 
         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)
 
+        o, o_mb = self.dec((z * y_mask)[:, :, :max_len], g=g)
+        return o, o_mb, 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 > 1, "n_speakers have to be larger than 1."
+        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)
+        o_hat, o_hat_mb = self.dec(z_hat * y_mask, g=g_tgt)
+        return o_hat, o_hat_mb, y_mask, (z, z_p, z_hat)
+    #'''