ttv_v1/t2w2v_transformer.py

import torch
from torch import nn
from torch.nn import functional as F
from ttv_v1 import modules
import attentions

from torch.nn import Conv1d, ConvTranspose1d
from torch.nn.utils import weight_norm, remove_weight_norm
from commons import init_weights

import typing as tp
import transformers
import math
from ttv_v1.styleencoder import StyleEncoder
import commons
from ttv_v1.modules import WN

def get_2d_padding(kernel_size: tp.Tuple[int, int], dilation: tp.Tuple[int, int] = (1, 1)):
    return (((kernel_size[0] - 1) * dilation[0]) // 2, ((kernel_size[1] - 1) * dilation[1]) // 2)

class Wav2vec2(torch.nn.Module):
    def __init__(self, layer=7):

        """we use the intermediate features of xls-r-300m.
           More specifically, we used the output from the 12th layer of the 24-layer transformer encoder.
        """
        super().__init__()

        # self.wav2vec2 = transformers.Wav2Vec2ForPreTraining.from_pretrained("facebook/wav2vec2-xls-r-300m")
        self.wav2vec2 = transformers.Wav2Vec2ForPreTraining.from_pretrained("facebook/mms-300m")

        for param in self.wav2vec2.parameters():
            param.requires_grad = False
            param.grad = None
        self.wav2vec2.eval()
        self.feature_layer = layer
    @torch.no_grad()
    def forward(self, x):
        """
        Args:
            x: torch.Tensor of shape (B x t)
        Returns:
            y: torch.Tensor of shape(B x C x t)
        """
        outputs = self.wav2vec2(x.squeeze(1), output_hidden_states=True)
        y = outputs.hidden_states[self.feature_layer]   
        y = y.permute((0, 2, 1))   
        return y

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.emb = nn.Embedding(n_vocab, hidden_channels)
    nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
    self.cond = nn.Conv1d(256, hidden_channels, 1)

    self.encoder = attentions.Encoder(
      hidden_channels,
      filter_channels,
      n_heads,
      n_layers,
      kernel_size,
      p_dropout)
    self.encoder2 = 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, g):
    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 = x + self.cond(g)
    x = self.encoder2(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 ResidualCouplingBlock_Transformer(nn.Module):
  def __init__(self,
      channels,
      hidden_channels,
      kernel_size,
      dilation_rate,
      n_layers=3,
      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.cond_block = torch.nn.Sequential(torch.nn.Linear(gin_channels, 4 * hidden_channels),
                                            nn.SiLU(), torch.nn.Linear(4 * hidden_channels, hidden_channels))

    self.flows = nn.ModuleList()
    for i in range(n_flows):
      self.flows.append(modules.ResidualCouplingLayer_Transformer_simple(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True, attention_head=4))
      self.flows.append(modules.Flip())

  def forward(self, x, x_mask, g=None, reverse=False):

    g = self.cond_block(g.squeeze(2))

    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_mask, g=None):

    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


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 W2VDecoder(nn.Module):
    def __init__(self,
                 in_channels,
                 hidden_channels,
                 kernel_size,
                 dilation_rate,
                 n_layers,
                 output_size=1024,
                 gin_channels=0,
                 p_dropout=0):
        super().__init__()
        self.in_channels = in_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.p_dropout = p_dropout

        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
        self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, p_dropout=p_dropout)
        self.proj = nn.Conv1d(hidden_channels, output_size, 1)

    def forward(self, x, x_mask, g=None):
        x = self.pre(x * x_mask) * x_mask
        x = self.enc(x, x_mask, g=g)
        x = self.proj(x) * x_mask

        return x


class PitchPredictor(nn.Module):
  def __init__(self):
    super().__init__()

    resblock_kernel_sizes = [3,5,7]
    upsample_rates = [2,2]
    initial_channel = 1024
    upsample_initial_channel = 256
    upsample_kernel_sizes = [4,4]
    resblock_dilation_sizes = [[1,3,5], [1,3,5], [1,3,5]]

    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

    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)

    self.cond = Conv1d(256, upsample_initial_channel, 1)

  def forward(self, x,  g):

    x = self.conv_pre(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)
    ## Predictor
    x = self.conv_post(x)

    return x
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,
    gin_channels=256,
    prosody_size=20,
    cfg=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.segment_size = segment_size
    self.mel_size = prosody_size

    self.enc_q = PosteriorEncoder(1024, inter_channels, hidden_channels, 5, 1, 16,  gin_channels=256)
    self.enc_p = TextEncoder(178, out_channels=inter_channels, hidden_channels=inter_channels, filter_channels=inter_channels*4, 
                                 n_heads=4, n_layers=3, kernel_size=9, p_dropout=0.2)
    
    self.flow = ResidualCouplingBlock_Transformer(inter_channels, hidden_channels, 5, 1, 3, gin_channels=256)

    self.w2v_decoder = W2VDecoder(inter_channels, inter_channels*2, 5, 1, 8, output_size=1024, p_dropout=0.1, gin_channels=256)    
    
    self.emb_g = StyleEncoder(in_dim=80, hidden_dim=256, out_dim=256)
    self.dp = StochasticDurationPredictor(inter_channels, inter_channels, 3, 0.5, 4, gin_channels=256)
    self.pp = PitchPredictor()
    self.phoneme_classifier = Conv1d(inter_channels, 178, 1, bias=False)

  @torch.no_grad()
  def infer(self, x, x_lengths, y_mel, y_length, noise_scale=1, noise_scale_w=1, length_scale=1):

    y_mask = torch.unsqueeze(commons.sequence_mask(y_length, y_mel.size(2)), 1).to(y_mel.dtype)

    # Speaker embedding from mel (Style Encoder)
    g = self.emb_g(y_mel, y_mask).unsqueeze(-1)
    
    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, g=g)


    logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)

      
    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)

    w2v = self.w2v_decoder(z, y_mask, g=g)
    pitch = self.pp(w2v, g)

    return w2v, pitch
  
  @torch.no_grad()
  def infer_noise_control(self, x, x_lengths, y_mel, y_length, noise_scale=0.333, noise_scale_w=1, length_scale=1, denoise_ratio = 0):

    y_mask = torch.unsqueeze(commons.sequence_mask(y_length, y_mel.size(2)), 1).to(y_mel.dtype)

    # Speaker embedding from mel (Style Encoder)
    g = self.emb_g(y_mel, y_mask).unsqueeze(-1)
    
    g_org, g_denoise = g[:1, :, :], g[1:, :, :]
    g = (1-denoise_ratio)*g_org + denoise_ratio*g_denoise


    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, g=g)


    logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
      
    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)

    w2v = self.w2v_decoder(z, y_mask, g=g)
    pitch = self.pp(w2v, g)

    return w2v, pitch