import math import numpy as np import torch from torch import nn from torch.nn import functional as F from torch.nn import Conv1d from torch.nn.utils import weight_norm, remove_weight_norm from module import commons from module.commons import init_weights, get_padding from module.transforms import piecewise_rational_quadratic_transform import torch.distributions as D LRELU_SLOPE = 0.1 class LayerNorm(nn.Module): def __init__(self, channels, eps=1e-5): super().__init__() self.channels = channels self.eps = eps self.gamma = nn.Parameter(torch.ones(channels)) self.beta = nn.Parameter(torch.zeros(channels)) def forward(self, x): x = x.transpose(1, -1) x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps) return x.transpose(1, -1) class ConvReluNorm(nn.Module): def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout): super().__init__() self.in_channels = in_channels self.hidden_channels = hidden_channels self.out_channels = out_channels self.kernel_size = kernel_size self.n_layers = n_layers self.p_dropout = p_dropout assert n_layers > 1, "Number of layers should be larger than 0." self.conv_layers = nn.ModuleList() self.norm_layers = nn.ModuleList() self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2)) self.norm_layers.append(LayerNorm(hidden_channels)) self.relu_drop = nn.Sequential( nn.ReLU(), nn.Dropout(p_dropout)) for _ in range(n_layers-1): self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2)) self.norm_layers.append(LayerNorm(hidden_channels)) self.proj = nn.Conv1d(hidden_channels, out_channels, 1) self.proj.weight.data.zero_() self.proj.bias.data.zero_() def forward(self, x, x_mask): x_org = x for i in range(self.n_layers): x = self.conv_layers[i](x * x_mask) x = self.norm_layers[i](x) x = self.relu_drop(x) x = x_org + self.proj(x) return x * x_mask class DDSConv(nn.Module): """ Dialted and Depth-Separable Convolution """ def __init__(self, channels, kernel_size, n_layers, p_dropout=0.): super().__init__() self.channels = channels self.kernel_size = kernel_size self.n_layers = n_layers self.p_dropout = p_dropout self.drop = nn.Dropout(p_dropout) self.convs_sep = nn.ModuleList() self.convs_1x1 = nn.ModuleList() self.norms_1 = nn.ModuleList() self.norms_2 = nn.ModuleList() for i in range(n_layers): dilation = kernel_size ** i padding = (kernel_size * dilation - dilation) // 2 self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, groups=channels, dilation=dilation, padding=padding )) self.convs_1x1.append(nn.Conv1d(channels, channels, 1)) self.norms_1.append(LayerNorm(channels)) self.norms_2.append(LayerNorm(channels)) def forward(self, x, x_mask, g=None): if g is not None: x = x + g for i in range(self.n_layers): y = self.convs_sep[i](x * x_mask) y = self.norms_1[i](y) y = F.gelu(y) y = self.convs_1x1[i](y) y = self.norms_2[i](y) y = F.gelu(y) y = self.drop(y) x = x + y return x * x_mask class WN(torch.nn.Module): def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0): super(WN, self).__init__() assert(kernel_size % 2 == 1) 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.in_layers = torch.nn.ModuleList() self.res_skip_layers = torch.nn.ModuleList() self.drop = nn.Dropout(p_dropout) if gin_channels != 0: cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1) self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight') for i in range(n_layers): dilation = dilation_rate ** i padding = int((kernel_size * dilation - dilation) / 2) in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size, dilation=dilation, padding=padding) in_layer = torch.nn.utils.weight_norm(in_layer, name='weight') self.in_layers.append(in_layer) # last one is not necessary if i < n_layers - 1: res_skip_channels = 2 * hidden_channels else: res_skip_channels = hidden_channels res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1) res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight') self.res_skip_layers.append(res_skip_layer) def forward(self, x, x_mask, g=None, **kwargs): output = torch.zeros_like(x) n_channels_tensor = torch.IntTensor([self.hidden_channels]) if g is not None: g = self.cond_layer(g) for i in range(self.n_layers): x_in = self.in_layers[i](x) if g is not None: cond_offset = i * 2 * self.hidden_channels g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:] else: g_l = torch.zeros_like(x_in) acts = commons.fused_add_tanh_sigmoid_multiply( x_in, g_l, n_channels_tensor) acts = self.drop(acts) res_skip_acts = self.res_skip_layers[i](acts) if i < self.n_layers - 1: res_acts = res_skip_acts[:,:self.hidden_channels,:] x = (x + res_acts) * x_mask output = output + res_skip_acts[:,self.hidden_channels:,:] else: output = output + res_skip_acts return output * x_mask def remove_weight_norm(self): if self.gin_channels != 0: torch.nn.utils.remove_weight_norm(self.cond_layer) for l in self.in_layers: torch.nn.utils.remove_weight_norm(l) for l in self.res_skip_layers: torch.nn.utils.remove_weight_norm(l) class ResBlock1(torch.nn.Module): def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): super(ResBlock1, self).__init__() self.convs1 = nn.ModuleList([ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], padding=get_padding(kernel_size, dilation[0]))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], padding=get_padding(kernel_size, dilation[1]))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], padding=get_padding(kernel_size, dilation[2]))) ]) self.convs1.apply(init_weights) self.convs2 = nn.ModuleList([ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))) ]) self.convs2.apply(init_weights) def forward(self, x, x_mask=None): for c1, c2 in zip(self.convs1, self.convs2): xt = F.leaky_relu(x, LRELU_SLOPE) if x_mask is not None: xt = xt * x_mask xt = c1(xt) xt = F.leaky_relu(xt, LRELU_SLOPE) if x_mask is not None: xt = xt * x_mask xt = c2(xt) x = xt + x if x_mask is not None: x = x * x_mask return x def remove_weight_norm(self): for l in self.convs1: remove_weight_norm(l) for l in self.convs2: remove_weight_norm(l) class ResBlock2(torch.nn.Module): def __init__(self, channels, kernel_size=3, dilation=(1, 3)): super(ResBlock2, self).__init__() self.convs = nn.ModuleList([ weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], padding=get_padding(kernel_size, dilation[0]))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], padding=get_padding(kernel_size, dilation[1]))) ]) self.convs.apply(init_weights) def forward(self, x, x_mask=None): for c in self.convs: xt = F.leaky_relu(x, LRELU_SLOPE) if x_mask is not None: xt = xt * x_mask xt = c(xt) x = xt + x if x_mask is not None: x = x * x_mask return x def remove_weight_norm(self): for l in self.convs: remove_weight_norm(l) class Log(nn.Module): def forward(self, x, x_mask, reverse=False, **kwargs): if not reverse: y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask logdet = torch.sum(-y, [1, 2]) return y, logdet else: x = torch.exp(x) * x_mask return x class Flip(nn.Module): def forward(self, x, *args, reverse=False, **kwargs): x = torch.flip(x, [1]) if not reverse: logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device) return x, logdet else: return x class ElementwiseAffine(nn.Module): def __init__(self, channels): super().__init__() self.channels = channels self.m = nn.Parameter(torch.zeros(channels,1)) self.logs = nn.Parameter(torch.zeros(channels,1)) def forward(self, x, x_mask, reverse=False, **kwargs): if not reverse: y = self.m + torch.exp(self.logs) * x y = y * x_mask logdet = torch.sum(self.logs * x_mask, [1,2]) return y, logdet else: x = (x - self.m) * torch.exp(-self.logs) * x_mask return x class ResidualCouplingLayer(nn.Module): def __init__(self, channels, hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=0, gin_channels=0, mean_only=False): assert channels % 2 == 0, "channels should be divisible by 2" super().__init__() self.channels = channels self.hidden_channels = hidden_channels self.kernel_size = kernel_size self.dilation_rate = dilation_rate self.n_layers = n_layers self.half_channels = channels // 2 self.mean_only = mean_only self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1) self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels) self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1) self.post.weight.data.zero_() self.post.bias.data.zero_() def forward(self, x, x_mask, g=None, reverse=False): x0, x1 = torch.split(x, [self.half_channels]*2, 1) h = self.pre(x0) * x_mask h = self.enc(h, x_mask, g=g) stats = self.post(h) * x_mask if not self.mean_only: m, logs = torch.split(stats, [self.half_channels]*2, 1) else: m = stats logs = torch.zeros_like(m) if not reverse: x1 = m + x1 * torch.exp(logs) * x_mask x = torch.cat([x0, x1], 1) logdet = torch.sum(logs, [1,2]) return x, logdet else: x1 = (x1 - m) * torch.exp(-logs) * x_mask x = torch.cat([x0, x1], 1) return x class ConvFlow(nn.Module): def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0): super().__init__() self.in_channels = in_channels self.filter_channels = filter_channels self.kernel_size = kernel_size self.n_layers = n_layers self.num_bins = num_bins self.tail_bound = tail_bound self.half_channels = in_channels // 2 self.pre = nn.Conv1d(self.half_channels, filter_channels, 1) self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.) self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1) self.proj.weight.data.zero_() self.proj.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) h = self.convs(h, x_mask, g=g) h = self.proj(h) * x_mask b, c, t = x0.shape h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?] unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels) unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels) unnormalized_derivatives = h[..., 2 * self.num_bins:] x1, logabsdet = piecewise_rational_quadratic_transform(x1, unnormalized_widths, unnormalized_heights, unnormalized_derivatives, inverse=reverse, tails='linear', tail_bound=self.tail_bound ) x = torch.cat([x0, x1], 1) * x_mask logdet = torch.sum(logabsdet * x_mask, [1,2]) if not reverse: return x, logdet else: return x class LinearNorm(nn.Module): def __init__(self, in_channels, out_channels, bias=True, spectral_norm=False, ): super(LinearNorm, self).__init__() self.fc = nn.Linear(in_channels, out_channels, bias) if spectral_norm: self.fc = nn.utils.spectral_norm(self.fc) def forward(self, input): out = self.fc(input) return out class Mish(nn.Module): def __init__(self): super(Mish, self).__init__() def forward(self, x): return x * torch.tanh(F.softplus(x)) class Conv1dGLU(nn.Module): ''' Conv1d + GLU(Gated Linear Unit) with residual connection. For GLU refer to https://arxiv.org/abs/1612.08083 paper. ''' def __init__(self, in_channels, out_channels, kernel_size, dropout): super(Conv1dGLU, self).__init__() self.out_channels = out_channels self.conv1 = ConvNorm(in_channels, 2 * out_channels, kernel_size=kernel_size) self.dropout = nn.Dropout(dropout) def forward(self, x): residual = x x = self.conv1(x) x1, x2 = torch.split(x, split_size_or_sections=self.out_channels, dim=1) x = x1 * torch.sigmoid(x2) x = residual + self.dropout(x) return x class ConvNorm(nn.Module): def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=None, dilation=1, bias=True, spectral_norm=False, ): super(ConvNorm, self).__init__() if padding is None: assert (kernel_size % 2 == 1) padding = int(dilation * (kernel_size - 1) / 2) self.conv = torch.nn.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=bias) if spectral_norm: self.conv = nn.utils.spectral_norm(self.conv) def forward(self, input): out = self.conv(input) return out class MultiHeadAttention(nn.Module): ''' Multi-Head Attention module ''' def __init__(self, n_head, d_model, d_k, d_v, dropout=0., spectral_norm=False): super().__init__() self.n_head = n_head self.d_k = d_k self.d_v = d_v self.w_qs = nn.Linear(d_model, n_head * d_k) self.w_ks = nn.Linear(d_model, n_head * d_k) self.w_vs = nn.Linear(d_model, n_head * d_v) self.attention = ScaledDotProductAttention(temperature=np.power(d_model, 0.5), dropout=dropout) self.fc = nn.Linear(n_head * d_v, d_model) self.dropout = nn.Dropout(dropout) if spectral_norm: self.w_qs = nn.utils.spectral_norm(self.w_qs) self.w_ks = nn.utils.spectral_norm(self.w_ks) self.w_vs = nn.utils.spectral_norm(self.w_vs) self.fc = nn.utils.spectral_norm(self.fc) def forward(self, x, mask=None): d_k, d_v, n_head = self.d_k, self.d_v, self.n_head sz_b, len_x, _ = x.size() residual = x q = self.w_qs(x).view(sz_b, len_x, n_head, d_k) k = self.w_ks(x).view(sz_b, len_x, n_head, d_k) v = self.w_vs(x).view(sz_b, len_x, n_head, d_v) q = q.permute(2, 0, 1, 3).contiguous().view(-1, len_x, d_k) # (n*b) x lq x dk k = k.permute(2, 0, 1, 3).contiguous().view(-1, len_x, d_k) # (n*b) x lk x dk v = v.permute(2, 0, 1, 3).contiguous().view(-1, len_x, d_v) # (n*b) x lv x dv if mask is not None: slf_mask = mask.repeat(n_head, 1, 1) # (n*b) x .. x .. else: slf_mask = None output, attn = self.attention(q, k, v, mask=slf_mask) output = output.view(n_head, sz_b, len_x, d_v) output = output.permute(1, 2, 0, 3).contiguous().view( sz_b, len_x, -1) # b x lq x (n*dv) output = self.fc(output) output = self.dropout(output) + residual return output, attn class ScaledDotProductAttention(nn.Module): ''' Scaled Dot-Product Attention ''' def __init__(self, temperature, dropout): super().__init__() self.temperature = temperature self.softmax = nn.Softmax(dim=2) self.dropout = nn.Dropout(dropout) def forward(self, q, k, v, mask=None): attn = torch.bmm(q, k.transpose(1, 2)) attn = attn / self.temperature if mask is not None: attn = attn.masked_fill(mask, -np.inf) attn = self.softmax(attn) p_attn = self.dropout(attn) output = torch.bmm(p_attn, v) return output, attn class MelStyleEncoder(nn.Module): ''' MelStyleEncoder ''' def __init__(self, n_mel_channels=80, style_hidden=128, style_vector_dim=256, style_kernel_size=5, style_head=2, dropout=0.1): super(MelStyleEncoder, self).__init__() self.in_dim = n_mel_channels self.hidden_dim = style_hidden self.out_dim = style_vector_dim self.kernel_size = style_kernel_size self.n_head = style_head self.dropout = dropout self.spectral = nn.Sequential( LinearNorm(self.in_dim, self.hidden_dim), Mish(), nn.Dropout(self.dropout), LinearNorm(self.hidden_dim, self.hidden_dim), Mish(), nn.Dropout(self.dropout) ) self.temporal = nn.Sequential( Conv1dGLU(self.hidden_dim, self.hidden_dim, self.kernel_size, self.dropout), Conv1dGLU(self.hidden_dim, self.hidden_dim, self.kernel_size, self.dropout), ) self.slf_attn = MultiHeadAttention(self.n_head, self.hidden_dim, self.hidden_dim // self.n_head, self.hidden_dim // self.n_head, self.dropout) self.fc = LinearNorm(self.hidden_dim, self.out_dim) def temporal_avg_pool(self, x, mask=None): if mask is None: out = torch.mean(x, dim=1) else: len_ = (~mask).sum(dim=1).unsqueeze(1) x = x.masked_fill(mask.unsqueeze(-1), 0) x = x.sum(dim=1) out = torch.div(x, len_) return out def forward(self, x, mask=None): x = x.transpose(1,2) if mask is not None: mask = (mask.int()==0).squeeze(1) max_len = x.shape[1] slf_attn_mask = mask.unsqueeze(1).expand(-1, max_len, -1) if mask is not None else None # spectral x = self.spectral(x) # temporal x = x.transpose(1, 2) x = self.temporal(x) x = x.transpose(1, 2) # self-attention if mask is not None: x = x.masked_fill(mask.unsqueeze(-1), 0) x, _ = self.slf_attn(x, mask=slf_attn_mask) # fc x = self.fc(x) # temoral average pooling w = self.temporal_avg_pool(x, mask=mask) return w.unsqueeze(-1) class MelStyleEncoderVAE(nn.Module): def __init__(self, spec_channels, z_latent_dim, emb_dim): super().__init__() self.ref_encoder = MelStyleEncoder(spec_channels, style_vector_dim=emb_dim) self.fc1 = nn.Linear(emb_dim, z_latent_dim) self.fc2 = nn.Linear(emb_dim, z_latent_dim) self.fc3 = nn.Linear(z_latent_dim, emb_dim) self.z_latent_dim = z_latent_dim def reparameterize(self, mu, logvar): if self.training: std = torch.exp(0.5 * logvar) eps = torch.randn_like(std) return eps.mul(std).add_(mu) else: return mu def forward(self, inputs, mask=None): enc_out = self.ref_encoder(inputs.squeeze(-1), mask).squeeze(-1) mu = self.fc1(enc_out) logvar = self.fc2(enc_out) posterior = D.Normal(mu, torch.exp(logvar)) kl_divergence = D.kl_divergence(posterior, D.Normal(torch.zeros_like(mu), torch.ones_like(logvar))) loss_kl = kl_divergence.mean() z = posterior.rsample() style_embed = self.fc3(z) return style_embed.unsqueeze(-1), loss_kl def infer(self, inputs=None, random_sample=False, manual_latent=None): if manual_latent is None: if random_sample: dev = next(self.parameters()).device posterior = D.Normal(torch.zeros(1, self.z_latent_dim, device=dev), torch.ones(1, self.z_latent_dim, device=dev)) z = posterior.rsample() else: enc_out = self.ref_encoder(inputs.transpose(1, 2)) mu = self.fc1(enc_out) z = mu else: z = manual_latent style_embed = self.fc3(z) return style_embed.unsqueeze(-1), z class ActNorm(nn.Module): def __init__(self, channels, ddi=False, **kwargs): super().__init__() self.channels = channels self.initialized = not ddi self.logs = nn.Parameter(torch.zeros(1, channels, 1)) self.bias = nn.Parameter(torch.zeros(1, channels, 1)) def forward(self, x, x_mask=None, g=None, reverse=False, **kwargs): if x_mask is None: x_mask = torch.ones(x.size(0), 1, x.size(2)).to(device=x.device, dtype=x.dtype) x_len = torch.sum(x_mask, [1, 2]) if not self.initialized: self.initialize(x, x_mask) self.initialized = True if reverse: z = (x - self.bias) * torch.exp(-self.logs) * x_mask logdet = None return z else: z = (self.bias + torch.exp(self.logs) * x) * x_mask logdet = torch.sum(self.logs) * x_len # [b] return z, logdet def store_inverse(self): pass def set_ddi(self, ddi): self.initialized = not ddi def initialize(self, x, x_mask): with torch.no_grad(): denom = torch.sum(x_mask, [0, 2]) m = torch.sum(x * x_mask, [0, 2]) / denom m_sq = torch.sum(x * x * x_mask, [0, 2]) / denom v = m_sq - (m ** 2) logs = 0.5 * torch.log(torch.clamp_min(v, 1e-6)) bias_init = (-m * torch.exp(-logs)).view(*self.bias.shape).to(dtype=self.bias.dtype) logs_init = (-logs).view(*self.logs.shape).to(dtype=self.logs.dtype) self.bias.data.copy_(bias_init) self.logs.data.copy_(logs_init) class InvConvNear(nn.Module): def __init__(self, channels, n_split=4, no_jacobian=False, **kwargs): super().__init__() assert (n_split % 2 == 0) self.channels = channels self.n_split = n_split self.no_jacobian = no_jacobian w_init = torch.linalg.qr(torch.FloatTensor(self.n_split, self.n_split).normal_())[0] if torch.det(w_init) < 0: w_init[:, 0] = -1 * w_init[:, 0] self.weight = nn.Parameter(w_init) def forward(self, x, x_mask=None, g=None, reverse=False, **kwargs): b, c, t = x.size() assert (c % self.n_split == 0) if x_mask is None: x_mask = 1 x_len = torch.ones((b,), dtype=x.dtype, device=x.device) * t else: x_len = torch.sum(x_mask, [1, 2]) x = x.view(b, 2, c // self.n_split, self.n_split // 2, t) x = x.permute(0, 1, 3, 2, 4).contiguous().view(b, self.n_split, c // self.n_split, t) if reverse: if hasattr(self, "weight_inv"): weight = self.weight_inv else: weight = torch.inverse(self.weight.float()).to(dtype=self.weight.dtype) logdet = None else: weight = self.weight if self.no_jacobian: logdet = 0 else: logdet = torch.logdet(self.weight) * (c / self.n_split) * x_len # [b] weight = weight.view(self.n_split, self.n_split, 1, 1) z = F.conv2d(x, weight) z = z.view(b, 2, self.n_split // 2, c // self.n_split, t) z = z.permute(0, 1, 3, 2, 4).contiguous().view(b, c, t) * x_mask if reverse: return z else: return z, logdet def store_inverse(self): self.weight_inv = torch.inverse(self.weight.float()).to(dtype=self.weight.dtype)