| import numpy as np |
| import torch |
| from torch import nn |
| from torch.nn import Conv1d |
| from torch.nn import functional as F |
| from torch.nn.utils import remove_weight_norm, weight_norm |
|
|
| from .commons import fused_add_tanh_sigmoid_multiply, get_padding, init_weights |
|
|
| 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 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) |
|
|
| |
| 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 = 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 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 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 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=0.0, 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) |
| k = k.permute(2, 0, 1, 3).contiguous().view(-1, len_x, d_k) |
| v = v.permute(2, 0, 1, 3).contiguous().view(-1, len_x, d_v) |
|
|
| if mask is not None: |
| slf_mask = mask.repeat(n_head, 1, 1) |
| 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) |
| ) |
|
|
| 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 |
| ) |
|
|
| |
| x = self.spectral(x) |
| |
| x = x.transpose(1, 2) |
| x = self.temporal(x) |
| x = x.transpose(1, 2) |
| |
| if mask is not None: |
| x = x.masked_fill(mask.unsqueeze(-1), 0) |
| x, _ = self.slf_attn(x, mask=slf_attn_mask) |
| |
| x = self.fc(x) |
| |
| w = self.temporal_avg_pool(x, mask=mask) |
|
|
| return w.unsqueeze(-1) |
|
|
