models.py

import copy
import math
import torch
from torch import nn
from torch.nn import functional as F

import commons
import modules
import attentions
import monotonic_align

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

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

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 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 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 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_ = 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()
    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 == "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.)
      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., 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
  # 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)