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

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
import math


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 TextEncoder(nn.Module):
  def __init__(self,
      n_vocab,
      out_channels,
      hidden_channels,
      filter_channels,
      n_heads,
      n_layers,
      kernel_size,
      p_dropout):
    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.encoder = attentions.Encoder(
      hidden_channels,
      filter_channels,
      n_heads,
      n_layers,
      kernel_size,
      p_dropout)
    self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1)

  def forward(self, x, x_lengths):
    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)
    stats = self.proj(x) * x_mask

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

class Encoder(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 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 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):
        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.cond = nn.Conv1d(256, 512, 1)
        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)
        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.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):
        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+1-i)//2,output_padding=1-i)))

        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.cond = nn.Conv1d(256, 512, 1)
        self.gen_istft_n_fft = gen_istft_n_fft
        self.gen_istft_hop_size = gen_istft_hop_size


    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)
      #print(x.device)
      pqmf = PQMF(x.device)
      
      x = self.conv_pre(x)#[B, ch, length]
      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.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):
        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+1-i)//2,output_padding=1-i)))#这里k和u不是成倍数的关系，对最终结果很有可能是有影响的，会有checkerboard artifacts的现象

        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.apply(init_weights)
        self.cond = nn.Conv1d(256, 512, 1)


    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)
      # pqmf = PQMF(x.device)

      x = self.conv_pre(x)#[B, ch, length]
      #print(x.size(),g.size())
      x = x + self.cond(g) # g [b, 256, 1] => cond(g) [b, 512, 1] 
      
      for i in range(self.num_upsamples):

          #print(x.size(),g.size())
          x = F.leaky_relu(x, modules.LRELU_SLOPE)
          #print(x.size(),g.size())
          x = self.ups[i](x)
          
          #print(x.size(),g.size())
          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
      #print(x.size(),g.size())    
      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]))
      #print(x.size(),g.size())
      spec = torch.exp(x[:,:,:self.post_n_fft // 2 + 1, :])
      phase = math.pi*torch.sin(x[:,:, self.post_n_fft // 2 + 1:, :])
      #print(spec.size(),phase.size())
      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])))
      #print(y_mb_hat.size())
      y_mb_hat = torch.reshape(y_mb_hat, (x.shape[0], self.subbands, 1, y_mb_hat.shape[-1]))
      #print(y_mb_hat.size())
      y_mb_hat = y_mb_hat.squeeze(-2)
      #print(y_mb_hat.size())
      y_mb_hat = F.conv_transpose1d(y_mb_hat, self.updown_filter* self.subbands, stride=self.subbands)#.cuda(x.device) * self.subbands, stride=self.subbands)
      #print(y_mb_hat.size())
      y_g_hat = self.multistream_conv_post(y_mb_hat)
      #print(y_g_hat.size(),y_mb_hat.size())
      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__()
        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 SpeakerEncoder(torch.nn.Module):
    def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256):
        super(SpeakerEncoder, self).__init__()
        self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True)
        self.linear = nn.Linear(model_hidden_size, model_embedding_size)
        self.relu = nn.ReLU()

    def forward(self, mels):
        self.lstm.flatten_parameters()
        _, (hidden, _) = self.lstm(mels)
        embeds_raw = self.relu(self.linear(hidden[-1]))
        return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
        
    def compute_partial_slices(self, total_frames, partial_frames, partial_hop):
        mel_slices = []
        for i in range(0, total_frames-partial_frames, partial_hop):
            mel_range = torch.arange(i, i+partial_frames)
            mel_slices.append(mel_range)
            
        return mel_slices
    
    def embed_utterance(self, mel, partial_frames=128, partial_hop=64):
        mel_len = mel.size(1)
        last_mel = mel[:,-partial_frames:]
        
        if mel_len > partial_frames:
            mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop)
            mels = list(mel[:,s] for s in mel_slices)
            mels.append(last_mel)
            mels = torch.stack(tuple(mels), 0).squeeze(1)
        
            with torch.no_grad():
                partial_embeds = self(mels)
            embed = torch.mean(partial_embeds, axis=0).unsqueeze(0)
            #embed = embed / torch.linalg.norm(embed, 2)
        else:
            with torch.no_grad():
                embed = self(last_mel)
        
        return embed

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

  def __init__(self, 
    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,
    gen_istft_n_fft,
    gen_istft_hop_size,
    n_speakers=0,
    gin_channels=0,
    use_sdp=False,
    ms_istft_vits=False,
    mb_istft_vits = False,
    subbands = False,
    istft_vits=False,
    **kwargs):

    super().__init__()
    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.ms_istft_vits = ms_istft_vits
    self.mb_istft_vits = mb_istft_vits
    self.istft_vits = istft_vits

    self.use_sdp = use_sdp

    self.enc_p = PosteriorEncoder(256, inter_channels, hidden_channels, 5, 1, 16)#768, inter_channels, hidden_channels, 5, 1, 16)
    
    if mb_istft_vits == True:
      print('Mutli-band iSTFT VITS')
      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)
    elif ms_istft_vits == True:
      print('Mutli-stream iSTFT VITS')
      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)
    elif istft_vits == True:
      print('iSTFT-VITS')
      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)
    else:
      print('Decoder Error in json file')

    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.enc_spk = SpeakerEncoder(model_hidden_size=gin_channels, model_embedding_size=gin_channels)

  def forward(self, c, spec, g=None, mel=None, c_lengths=None, spec_lengths=None):
    if c_lengths == None:
      c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
    if spec_lengths == None:
      spec_lengths = (torch.ones(spec.size(0)) * spec.size(-1)).to(spec.device)
      
    g = self.enc_spk(mel.transpose(1,2))
    g = g.unsqueeze(-1)
      
    _, m_p, logs_p, _ = self.enc_p(c, c_lengths)
    z, m_q, logs_q, spec_mask = self.enc_q(spec, spec_lengths, g=g) 
    z_p = self.flow(z, spec_mask, g=g)

    z_slice, ids_slice = commons.rand_slice_segments(z, spec_lengths, self.segment_size)
    o, o_mb = self.dec(z_slice, g=g)
    
    return o, o_mb, ids_slice, spec_mask, (z, z_p, m_p, logs_p, m_q, logs_q)

  def infer(self, c, g=None, mel=None, c_lengths=None):
    if c_lengths == None:
      c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
    g = self.enc_spk.embed_utterance(mel.transpose(1,2))
    g = g.unsqueeze(-1)

    z_p, m_p, logs_p, c_mask = self.enc_p(c, c_lengths)
    z = self.flow(z_p, c_mask, g=g, reverse=True)
    o,o_mb = self.dec(z * c_mask, g=g)
    
    return o