everythingfades/vits_personal_exploration

1import math2import torch3from torch import nn4from torch.nn import functional as F5 6import commons7import modules8import attentions9import monotonic_align10 11from torch.nn import Conv1d, ConvTranspose1d, Conv2d12from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm13from commons import init_weights, get_padding14 15 16class StochasticDurationPredictor(nn.Module):17  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):18    super().__init__()19    filter_channels = in_channels # it needs to be removed from future version.20    self.in_channels = in_channels21    self.filter_channels = filter_channels22    self.kernel_size = kernel_size23    self.p_dropout = p_dropout24    self.n_flows = n_flows25    self.gin_channels = gin_channels26 27    self.log_flow = modules.Log()28    self.flows = nn.ModuleList()29    self.flows.append(modules.ElementwiseAffine(2))30    for i in range(n_flows):31      self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))32      self.flows.append(modules.Flip())33 34    self.post_pre = nn.Conv1d(1, filter_channels, 1)35    self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)36    self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)37    self.post_flows = nn.ModuleList()38    self.post_flows.append(modules.ElementwiseAffine(2))39    for i in range(4):40      self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))41      self.post_flows.append(modules.Flip())42 43    self.pre = nn.Conv1d(in_channels, filter_channels, 1)44    self.proj = nn.Conv1d(filter_channels, filter_channels, 1)45    self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)46    if gin_channels != 0:47      self.cond = nn.Conv1d(gin_channels, filter_channels, 1)48 49  def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):50    x = torch.detach(x)51    x = self.pre(x)52    if g is not None:53      g = torch.detach(g)54      x = x + self.cond(g)55    x = self.convs(x, x_mask)56    x = self.proj(x) * x_mask57 58    if not reverse:59      flows = self.flows60      assert w is not None61 62      logdet_tot_q = 0 63      h_w = self.post_pre(w)64      h_w = self.post_convs(h_w, x_mask)65      h_w = self.post_proj(h_w) * x_mask66      e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask67      z_q = e_q68      for flow in self.post_flows:69        z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))70        logdet_tot_q += logdet_q71      z_u, z1 = torch.split(z_q, [1, 1], 1) 72      u = torch.sigmoid(z_u) * x_mask73      z0 = (w - u) * x_mask74      logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2])75      logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q76 77      logdet_tot = 078      z0, logdet = self.log_flow(z0, x_mask)79      logdet_tot += logdet80      z = torch.cat([z0, z1], 1)81      for flow in flows:82        z, logdet = flow(z, x_mask, g=x, reverse=reverse)83        logdet_tot = logdet_tot + logdet84      nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot85      return nll + logq # [b]86    else:87      flows = list(reversed(self.flows))88      flows = flows[:-2] + [flows[-1]] # remove a useless vflow89      z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale90      for flow in flows:91        z = flow(z, x_mask, g=x, reverse=reverse)92      z0, z1 = torch.split(z, [1, 1], 1)93      logw = z094      return logw95 96 97class DurationPredictor(nn.Module):98  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):99    super().__init__()100 101    self.in_channels = in_channels102    self.filter_channels = filter_channels103    self.kernel_size = kernel_size104    self.p_dropout = p_dropout105    self.gin_channels = gin_channels106 107    self.drop = nn.Dropout(p_dropout)108    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)109    self.norm_1 = modules.LayerNorm(filter_channels)110    self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)111    self.norm_2 = modules.LayerNorm(filter_channels)112    self.proj = nn.Conv1d(filter_channels, 1, 1)113 114    if gin_channels != 0:115      self.cond = nn.Conv1d(gin_channels, in_channels, 1)116 117  def forward(self, x, x_mask, g=None):118    x = torch.detach(x)119    if g is not None:120      g = torch.detach(g)121      x = x + self.cond(g)122    x = self.conv_1(x * x_mask)123    x = torch.relu(x)124    x = self.norm_1(x)125    x = self.drop(x)126    x = self.conv_2(x * x_mask)127    x = torch.relu(x)128    x = self.norm_2(x)129    x = self.drop(x)130    x = self.proj(x * x_mask)131    return x * x_mask132 133 134class TextEncoder(nn.Module):135  def __init__(self,136      n_vocab,137      out_channels,138      hidden_channels,139      filter_channels,140      n_heads,141      n_layers,142      kernel_size,143      p_dropout):144    super().__init__()145    self.n_vocab = n_vocab146    self.out_channels = out_channels147    self.hidden_channels = hidden_channels148    self.filter_channels = filter_channels149    self.n_heads = n_heads150    self.n_layers = n_layers151    self.kernel_size = kernel_size152    self.p_dropout = p_dropout153 154    self.emb = nn.Embedding(n_vocab, hidden_channels)155    nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)156 157    self.encoder = attentions.Encoder(158      hidden_channels,159      filter_channels,160      n_heads,161      n_layers,162      kernel_size,163      p_dropout)164    self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1)165 166  def forward(self, x, x_lengths):167    x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]168    x = torch.transpose(x, 1, -1) # [b, h, t]169    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)170 171    x = self.encoder(x * x_mask, x_mask)172    stats = self.proj(x) * x_mask173 174    m, logs = torch.split(stats, self.out_channels, dim=1)175    return x, m, logs, x_mask176 177 178class ResidualCouplingBlock(nn.Module):179  def __init__(self,180      channels,181      hidden_channels,182      kernel_size,183      dilation_rate,184      n_layers,185      n_flows=4,186      gin_channels=0):187    super().__init__()188    self.channels = channels189    self.hidden_channels = hidden_channels190    self.kernel_size = kernel_size191    self.dilation_rate = dilation_rate192    self.n_layers = n_layers193    self.n_flows = n_flows194    self.gin_channels = gin_channels195 196    self.flows = nn.ModuleList()197    for i in range(n_flows):198      self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))199      self.flows.append(modules.Flip())200 201  def forward(self, x, x_mask, g=None, reverse=False):202    if not reverse:203      for flow in self.flows:204        x, _ = flow(x, x_mask, g=g, reverse=reverse)205    else:206      for flow in reversed(self.flows):207        x = flow(x, x_mask, g=g, reverse=reverse)208    return x209 210 211class PosteriorEncoder(nn.Module):212  def __init__(self,213      in_channels,214      out_channels,215      hidden_channels,216      kernel_size,217      dilation_rate,218      n_layers,219      gin_channels=0):220    super().__init__()221    self.in_channels = in_channels222    self.out_channels = out_channels223    self.hidden_channels = hidden_channels224    self.kernel_size = kernel_size225    self.dilation_rate = dilation_rate226    self.n_layers = n_layers227    self.gin_channels = gin_channels228 229    self.pre = nn.Conv1d(in_channels, hidden_channels, 1)230    self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)231    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)232 233  def forward(self, x, x_lengths, g=None):234    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)235    x = self.pre(x) * x_mask236    x = self.enc(x, x_mask, g=g)237    stats = self.proj(x) * x_mask238    m, logs = torch.split(stats, self.out_channels, dim=1)239    z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask240    return z, m, logs, x_mask241 242 243class Generator(torch.nn.Module):244    def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):245        super(Generator, self).__init__()246        self.num_kernels = len(resblock_kernel_sizes)247        self.num_upsamples = len(upsample_rates)248        self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)249        resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2250 251        self.ups = nn.ModuleList()252        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):253            self.ups.append(weight_norm(254                ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),255                                k, u, padding=(k-u)//2)))256 257        self.resblocks = nn.ModuleList()258        for i in range(len(self.ups)):259            ch = upsample_initial_channel//(2**(i+1))260            for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):261                self.resblocks.append(resblock(ch, k, d))262 263        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)264        self.ups.apply(init_weights)265 266        if gin_channels != 0:267            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)268 269    def forward(self, x, g=None):270        x = self.conv_pre(x)271        if g is not None:272          x = x + self.cond(g)273 274        for i in range(self.num_upsamples):275            x = F.leaky_relu(x, modules.LRELU_SLOPE)276            x = self.ups[i](x)277            xs = None278            for j in range(self.num_kernels):279                if xs is None:280                    xs = self.resblocks[i*self.num_kernels+j](x)281                else:282                    xs += self.resblocks[i*self.num_kernels+j](x)283            x = xs / self.num_kernels284        x = F.leaky_relu(x)285        x = self.conv_post(x)286        x = torch.tanh(x)287 288        return x289 290    def remove_weight_norm(self):291        print('Removing weight norm...')292        for l in self.ups:293            remove_weight_norm(l)294        for l in self.resblocks:295            l.remove_weight_norm()296 297 298class DiscriminatorP(torch.nn.Module):299    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):300        super(DiscriminatorP, self).__init__()301        self.period = period302        self.use_spectral_norm = use_spectral_norm303        norm_f = weight_norm if use_spectral_norm == False else spectral_norm304        self.convs = nn.ModuleList([305            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),306            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),307            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),308            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),309            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),310        ])311        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))312 313    def forward(self, x):314        fmap = []315 316        # 1d to 2d317        b, c, t = x.shape318        if t % self.period != 0: # pad first319            n_pad = self.period - (t % self.period)320            x = F.pad(x, (0, n_pad), "reflect")321            t = t + n_pad322        x = x.view(b, c, t // self.period, self.period)323 324        for l in self.convs:325            x = l(x)326            x = F.leaky_relu(x, modules.LRELU_SLOPE)327            fmap.append(x)328        x = self.conv_post(x)329        fmap.append(x)330        x = torch.flatten(x, 1, -1)331 332        return x, fmap333 334 335class DiscriminatorS(torch.nn.Module):336    def __init__(self, use_spectral_norm=False):337        super(DiscriminatorS, self).__init__()338        norm_f = weight_norm if use_spectral_norm == False else spectral_norm339        self.convs = nn.ModuleList([340            norm_f(Conv1d(1, 16, 15, 1, padding=7)),341            norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),342            norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),343            norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),344            norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),345            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),346        ])347        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))348 349    def forward(self, x):350        fmap = []351 352        for l in self.convs:353            x = l(x)354            x = F.leaky_relu(x, modules.LRELU_SLOPE)355            fmap.append(x)356        x = self.conv_post(x)357        fmap.append(x)358        x = torch.flatten(x, 1, -1)359 360        return x, fmap361 362 363class MultiPeriodDiscriminator(torch.nn.Module):364    def __init__(self, use_spectral_norm=False):365        super(MultiPeriodDiscriminator, self).__init__()366        periods = [2,3,5,7,11]367 368        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]369        discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]370        self.discriminators = nn.ModuleList(discs)371 372    def forward(self, y, y_hat):373        y_d_rs = []374        y_d_gs = []375        fmap_rs = []376        fmap_gs = []377        for i, d in enumerate(self.discriminators):378            y_d_r, fmap_r = d(y)379            y_d_g, fmap_g = d(y_hat)380            y_d_rs.append(y_d_r)381            y_d_gs.append(y_d_g)382            fmap_rs.append(fmap_r)383            fmap_gs.append(fmap_g)384 385        return y_d_rs, y_d_gs, fmap_rs, fmap_gs386 387 388 389class SynthesizerTrn(nn.Module):390  """391  Synthesizer for Training392  """393 394  def __init__(self, 395    n_vocab,396    spec_channels,397    segment_size,398    inter_channels,399    hidden_channels,400    filter_channels,401    n_heads,402    n_layers,403    kernel_size,404    p_dropout,405    resblock, 406    resblock_kernel_sizes, 407    resblock_dilation_sizes, 408    upsample_rates, 409    upsample_initial_channel, 410    upsample_kernel_sizes,411    n_speakers=0,412    gin_channels=0,413    use_sdp=True,414    **kwargs):415 416    super().__init__()417    self.n_vocab = n_vocab418    self.spec_channels = spec_channels419    self.inter_channels = inter_channels420    self.hidden_channels = hidden_channels421    self.filter_channels = filter_channels422    self.n_heads = n_heads423    self.n_layers = n_layers424    self.kernel_size = kernel_size425    self.p_dropout = p_dropout426    self.resblock = resblock427    self.resblock_kernel_sizes = resblock_kernel_sizes428    self.resblock_dilation_sizes = resblock_dilation_sizes429    self.upsample_rates = upsample_rates430    self.upsample_initial_channel = upsample_initial_channel431    self.upsample_kernel_sizes = upsample_kernel_sizes432    self.segment_size = segment_size433    self.n_speakers = n_speakers434    self.gin_channels = gin_channels435 436    self.use_sdp = use_sdp437 438    self.enc_p = TextEncoder(n_vocab,439        inter_channels,440        hidden_channels,441        filter_channels,442        n_heads,443        n_layers,444        kernel_size,445        p_dropout)446    self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)447    self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)448    self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)449 450    if use_sdp:451      self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)452    else:453      self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)454 455    if n_speakers > 1:456      self.emb_g = nn.Embedding(n_speakers, gin_channels)457 458  def forward(self, x, x_lengths, y, y_lengths, sid=None):459 460    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)461    if self.n_speakers > 0:462      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]463    else:464      g = None465 466    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)467    z_p = self.flow(z, y_mask, g=g)468 469    with torch.no_grad():470      # negative cross-entropy471      s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]472      neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s]473      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]474      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]475      neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s]476      neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4477 478      attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)479      attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()480 481    w = attn.sum(2)482    if self.use_sdp:483      l_length = self.dp(x, x_mask, w, g=g)484      l_length = l_length / torch.sum(x_mask)485    else:486      logw_ = torch.log(w + 1e-6) * x_mask487      logw = self.dp(x, x_mask, g=g)488      l_length = torch.sum((logw - logw_)**2, [1,2]) / torch.sum(x_mask) # for averaging 489 490    # expand prior491    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)492    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)493 494    z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)495    o = self.dec(z_slice, g=g)496    return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)497 498  def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None):499    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)500    if self.n_speakers > 0:501      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]502    else:503      g = None504 505    if self.use_sdp:506      logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)507    else:508      logw = self.dp(x, x_mask, g=g)509    w = torch.exp(logw) * x_mask * length_scale510    w_ceil = torch.ceil(w)511    y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()512    y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)513    attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)514    attn = commons.generate_path(w_ceil, attn_mask)515 516    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']517    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']518 519    z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale520    z = self.flow(z_p, y_mask, g=g, reverse=True)521    o = self.dec((z * y_mask)[:,:,:max_len], g=g)522    return o, attn, y_mask, (z, z_p, m_p, logs_p)523 524  def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):525    assert self.n_speakers > 0, "n_speakers have to be larger than 0."526    g_src = self.emb_g(sid_src).unsqueeze(-1)527    g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)528    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)529    z_p = self.flow(z, y_mask, g=g_src)530    z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)531    o_hat = self.dec(z_hat * y_mask, g=g_tgt)532    return o_hat, y_mask, (z, z_p, z_hat)533 534