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| import copy | |
| import math | |
| import torch | |
| from torch import nn | |
| from torch.nn import functional as F | |
| import commons | |
| import modules | |
| import attentions | |
| import monotonic_align | |
| 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, OnnxSTFT | |
| AVAILABLE_FLOW_TYPES = ["pre_conv", "pre_conv2", "fft", "mono_layer_inter_residual", "mono_layer_post_residual"] | |
| AVAILABLE_DURATION_DISCRIMINATOR_TYPES = {"dur_disc_1": "DurationDiscriminator", "dur_disc_2": "DurationDiscriminator2"} | |
| 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 DurationDiscriminator(nn.Module): # vits2 | |
| # TODO : not using "spk conditioning" for now according to the paper. | |
| # Can be a better discriminator if we use it. | |
| 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.dur_proj = nn.Conv1d(1, filter_channels, 1) | |
| self.pre_out_conv_1 = nn.Conv1d(2 * filter_channels, filter_channels, kernel_size, padding=kernel_size // 2) | |
| self.pre_out_norm_1 = modules.LayerNorm(filter_channels) | |
| self.pre_out_conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2) | |
| self.pre_out_norm_2 = modules.LayerNorm(filter_channels) | |
| # if gin_channels != 0: | |
| # self.cond = nn.Conv1d(gin_channels, in_channels, 1) | |
| self.output_layer = nn.Sequential( | |
| nn.Linear(filter_channels, 1), | |
| nn.Sigmoid() | |
| ) | |
| def forward_probability(self, x, x_mask, dur, g=None): | |
| dur = self.dur_proj(dur) | |
| x = torch.cat([x, dur], dim=1) | |
| x = self.pre_out_conv_1(x * x_mask) | |
| # x = torch.relu(x) | |
| # x = self.pre_out_norm_1(x) | |
| # x = self.drop(x) | |
| x = self.pre_out_conv_2(x * x_mask) | |
| # x = torch.relu(x) | |
| # x = self.pre_out_norm_2(x) | |
| # x = self.drop(x) | |
| x = x * x_mask | |
| x = x.transpose(1, 2) | |
| output_prob = self.output_layer(x) | |
| return output_prob | |
| def forward(self, x, x_mask, dur_r, dur_hat, 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) | |
| output_probs = [] | |
| for dur in [dur_r, dur_hat]: | |
| output_prob = self.forward_probability(x, x_mask, dur, g) | |
| output_probs.append(output_prob) | |
| return output_probs | |
| class DurationDiscriminator2(nn.Module): # vits2 - DurationDiscriminator2 | |
| # TODO : not using "spk conditioning" for now according to the paper. | |
| # Can be a better discriminator if we use it. | |
| 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.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.dur_proj = nn.Conv1d(1, filter_channels, 1) | |
| self.pre_out_conv_1 = nn.Conv1d( | |
| 2 * filter_channels, filter_channels, kernel_size, padding=kernel_size // 2 | |
| ) | |
| self.pre_out_norm_1 = modules.LayerNorm(filter_channels) | |
| self.pre_out_conv_2 = nn.Conv1d( | |
| filter_channels, filter_channels, kernel_size, padding=kernel_size // 2 | |
| ) | |
| self.pre_out_norm_2 = modules.LayerNorm(filter_channels) | |
| # if gin_channels != 0: | |
| # self.cond = nn.Conv1d(gin_channels, in_channels, 1) | |
| self.output_layer = nn.Sequential(nn.Linear(filter_channels, 1), nn.Sigmoid()) | |
| def forward_probability(self, x, x_mask, dur, g=None): | |
| dur = self.dur_proj(dur) | |
| x = torch.cat([x, dur], dim=1) | |
| x = self.pre_out_conv_1(x * x_mask) | |
| x = torch.relu(x) | |
| x = self.pre_out_norm_1(x) | |
| x = self.pre_out_conv_2(x * x_mask) | |
| x = torch.relu(x) | |
| x = self.pre_out_norm_2(x) | |
| x = x * x_mask | |
| x = x.transpose(1, 2) | |
| output_prob = self.output_layer(x) | |
| return output_prob | |
| def forward(self, x, x_mask, dur_r, dur_hat, 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.conv_2(x * x_mask) | |
| x = torch.relu(x) | |
| x = self.norm_2(x) | |
| output_probs = [] | |
| for dur in [dur_r, dur_hat]: | |
| output_prob = self.forward_probability(x, x_mask, dur, g) | |
| output_probs.append([output_prob]) | |
| return output_probs | |
| class TextEncoder(nn.Module): | |
| def __init__(self, | |
| n_vocab, | |
| out_channels, | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| p_dropout, | |
| gin_channels=0): | |
| 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.gin_channels = gin_channels | |
| self.emb = nn.Embedding(n_vocab, hidden_channels) | |
| nn.init.normal_(self.emb.weight, 0.0, hidden_channels ** -0.5) | |
| self.encoder = attentions.Encoder( | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| p_dropout, | |
| gin_channels=self.gin_channels) | |
| self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) | |
| def forward(self, x, x_lengths, g=None): | |
| 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, g=g) | |
| stats = self.proj(x) * x_mask | |
| m, logs = torch.split(stats, self.out_channels, dim=1) | |
| return x, m, logs, x_mask | |
| class ResidualCouplingTransformersLayer2(nn.Module): # vits2 | |
| 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.pre_transformer = attentions.Encoder( | |
| hidden_channels, | |
| hidden_channels, | |
| n_heads=2, | |
| n_layers=1, | |
| kernel_size=kernel_size, | |
| p_dropout=p_dropout, | |
| # window_size=None, | |
| ) | |
| self.enc = modules.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 = h + self.pre_transformer(h * x_mask, x_mask) # vits2 residual connection | |
| 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 ResidualCouplingTransformersLayer(nn.Module): # vits2 | |
| 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 | |
| # vits2 | |
| self.pre_transformer = attentions.Encoder( | |
| self.half_channels, | |
| self.half_channels, | |
| n_heads=2, | |
| n_layers=2, | |
| kernel_size=3, | |
| p_dropout=0.1, | |
| window_size=None | |
| ) | |
| self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1) | |
| self.enc = modules.WN( | |
| hidden_channels, | |
| kernel_size, | |
| dilation_rate, | |
| n_layers, | |
| p_dropout=p_dropout, | |
| gin_channels=gin_channels, | |
| ) | |
| # vits2 | |
| self.post_transformer = attentions.Encoder( | |
| self.hidden_channels, | |
| self.hidden_channels, | |
| n_heads=2, | |
| n_layers=2, | |
| kernel_size=3, | |
| p_dropout=0.1, | |
| window_size=None | |
| ) | |
| 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) | |
| x0_ = self.pre_transformer(x0 * x_mask, x_mask) # vits2 | |
| x0_ = x0_ + x0 # vits2 residual connection | |
| h = self.pre(x0_) * x_mask # changed from x0 to x0_ to retain x0 for the flow | |
| h = self.enc(h, x_mask, g=g) | |
| # vits2 - (experimental;uncomment the following 2 line to use) | |
| # h_ = self.post_transformer(h, x_mask) | |
| # h = h + h_ #vits2 residual connection | |
| 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 | |
| def remove_weight_norm(self): # ! | |
| self.enc.remove_weight_norm() | |
| class FFTransformerCouplingLayer(nn.Module): # vits2 | |
| def __init__(self, | |
| channels, | |
| hidden_channels, | |
| kernel_size, | |
| n_layers, | |
| n_heads, | |
| p_dropout=0, | |
| filter_channels=768, | |
| mean_only=False, | |
| gin_channels=0 | |
| ): | |
| 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.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 = attentions.FFT( | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| p_dropout, | |
| isflow=True, | |
| 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) | |
| h = h_ + h | |
| 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 MonoTransformerFlowLayer(nn.Module): # vits2 | |
| def __init__( | |
| self, | |
| channels, | |
| hidden_channels, | |
| mean_only=False, | |
| residual_connection=False, | |
| # according to VITS-2 paper fig 1B set residual_connection=True | |
| ): | |
| assert channels % 2 == 0, "channels should be divisible by 2" | |
| super().__init__() | |
| self.channels = channels | |
| self.hidden_channels = hidden_channels | |
| self.half_channels = channels // 2 | |
| self.mean_only = mean_only | |
| self.residual_connection = residual_connection | |
| # vits2 | |
| self.pre_transformer = attentions.Encoder( | |
| self.half_channels, | |
| self.half_channels, | |
| n_heads=2, | |
| n_layers=2, | |
| kernel_size=3, | |
| p_dropout=0.1, | |
| window_size=None | |
| ) | |
| self.post = nn.Conv1d(self.half_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): | |
| if self.residual_connection: | |
| if not reverse: | |
| x0, x1 = torch.split(x, [self.half_channels] * 2, 1) | |
| x0_ = x0 * x_mask | |
| x0_ = self.pre_transformer(x0, x_mask) # vits2 | |
| stats = self.post(x0_) * 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) | |
| x1 = m + x1 * torch.exp(logs) * x_mask | |
| x_ = torch.cat([x0, x1], 1) | |
| x = x + x_ | |
| logdet = torch.sum(torch.log(torch.exp(logs) + 1), [1, 2]) | |
| logdet = logdet + torch.log(torch.tensor(2)) * (x0.shape[1] * x0.shape[2]) | |
| return x, logdet | |
| else: | |
| x0, x1 = torch.split(x, [self.half_channels] * 2, 1) | |
| x0 = x0 / 2 | |
| x0_ = x0 * x_mask | |
| x0_ = self.pre_transformer(x0, x_mask) # vits2 | |
| stats = self.post(x0_) * 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) | |
| x1_ = ((x1 - m) / (1 + torch.exp(-logs))) * x_mask | |
| x = torch.cat([x0, x1_], 1) | |
| return x | |
| else: | |
| x0, x1 = torch.split(x, [self.half_channels] * 2, 1) | |
| x0_ = self.pre_transformer(x0 * x_mask, x_mask) # vits2 | |
| h = x0_ + x0 # vits2 | |
| 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 ResidualCouplingTransformersBlock(nn.Module): # vits2 | |
| def __init__(self, | |
| channels, | |
| hidden_channels, | |
| kernel_size, | |
| dilation_rate, | |
| n_layers, | |
| n_flows=4, | |
| gin_channels=0, | |
| use_transformer_flows=False, | |
| transformer_flow_type="pre_conv", | |
| ): | |
| 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() | |
| # TODO : clean up this mess | |
| if use_transformer_flows: | |
| if transformer_flow_type == "pre_conv": | |
| for i in range(n_flows): | |
| self.flows.append( | |
| ResidualCouplingTransformersLayer( | |
| channels, | |
| hidden_channels, | |
| kernel_size, | |
| dilation_rate, | |
| n_layers, | |
| gin_channels=gin_channels, | |
| mean_only=True | |
| ) | |
| ) | |
| self.flows.append(modules.Flip()) | |
| elif transformer_flow_type == "pre_conv2": | |
| for i in range(n_flows): | |
| self.flows.append( | |
| ResidualCouplingTransformersLayer2( | |
| channels, | |
| hidden_channels, | |
| kernel_size, | |
| dilation_rate, | |
| n_layers, | |
| gin_channels=gin_channels, | |
| mean_only=True, | |
| ) | |
| ) | |
| self.flows.append(modules.Flip()) | |
| elif transformer_flow_type == "fft": | |
| for i in range(n_flows): | |
| self.flows.append( | |
| FFTransformerCouplingLayer( | |
| channels, | |
| hidden_channels, | |
| kernel_size, | |
| dilation_rate, | |
| n_layers, | |
| gin_channels=gin_channels, | |
| mean_only=True | |
| ) | |
| ) | |
| self.flows.append(modules.Flip()) | |
| elif transformer_flow_type == "mono_layer_inter_residual": | |
| 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()) | |
| self.flows.append( | |
| MonoTransformerFlowLayer( | |
| channels, hidden_channels, mean_only=True | |
| ) | |
| ) | |
| elif transformer_flow_type == "mono_layer_post_residual": | |
| 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()) | |
| self.flows.append( | |
| MonoTransformerFlowLayer( | |
| channels, hidden_channels, mean_only=True, | |
| residual_connection=True | |
| ) | |
| ) | |
| else: | |
| 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 | |
| def remove_weight_norm(self): # ! | |
| for i, l in enumerate(self.flows): | |
| if i % 2 == 0: | |
| l.remove_weight_norm() | |
| 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 | |
| def remove_weight_norm(self): # ! | |
| for i, l in enumerate(self.flows): | |
| if i % 2 == 0: | |
| l.remove_weight_norm() | |
| 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 Generator(torch.nn.Module): | |
| def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, | |
| upsample_initial_channel, upsample_kernel_sizes, gin_channels=0): | |
| super(Generator, self).__init__() | |
| 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 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.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) | |
| self.ups.apply(init_weights) | |
| if gin_channels != 0: | |
| self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) | |
| def forward(self, x, g=None): | |
| x = self.conv_pre(x) | |
| if g is not None: | |
| 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.conv_post(x) | |
| x = torch.tanh(x) | |
| return x | |
| 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 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, is_onnx=False): | |
| 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.stft = TorchSTFT(filter_length=self.gen_istft_n_fft, hop_length=self.gen_istft_hop_size, | |
| win_length=self.gen_istft_n_fft) | |
| ''' | |
| # - for onnx | |
| if is_onnx == True: | |
| self.stft = OnnxSTFT(filter_length=self.gen_istft_n_fft, hop_length=self.gen_istft_hop_size, | |
| win_length=self.gen_istft_n_fft) | |
| else: | |
| 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) | |
| 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, is_onnx=False): | |
| 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) // 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 = 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 | |
| #- for onnx | |
| if is_onnx == True: | |
| self.stft = OnnxSTFT(filter_length=self.gen_istft_n_fft, hop_length=self.gen_istft_hop_size, win_length=self.gen_istft_n_fft) | |
| else: | |
| 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): | |
| ''' | |
| 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) # ! | |
| ''' | |
| stft = self.stft.to(x.device) | |
| pqmf = PQMF(x.device) | |
| x = self.conv_pre(x) # [B, ch, length] | |
| 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, is_onnx=False): | |
| 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) // 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 = 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 = weight_norm(Conv1d(self.subbands, 1, kernel_size=63, bias=False, padding=get_padding(63, 1))) # from MB-iSTFT-VITS-44100-Ja | |
| self.multistream_conv_post.apply(init_weights) | |
| #- for onnx | |
| if is_onnx == True: | |
| self.stft = OnnxSTFT(filter_length=self.gen_istft_n_fft, hop_length=self.gen_istft_hop_size, win_length=self.gen_istft_n_fft) | |
| else: | |
| 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): | |
| ''' | |
| 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) # ! | |
| ''' | |
| stft = self.stft.to(x.device) | |
| # pqmf = PQMF(x.device) | |
| x = self.conv_pre(x) # [B, ch, length] | |
| 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_mb_hat = F.conv_transpose1d(y_mb_hat, self.updown_filter.cuda(x.device) * self.subbands, stride=self.subbands) | |
| y_mb_hat = F.conv_transpose1d(y_mb_hat, self.updown_filter.to(x.device) * self.subbands, stride=self.subbands) | |
| y_g_hat = self.multistream_conv_post(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 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 SynthesizerTrn(nn.Module): | |
| """ | |
| Synthesizer for Training | |
| """ | |
| def __init__(self, | |
| n_vocab, | |
| 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=True, | |
| ms_istft_vits=False, | |
| mb_istft_vits=False, | |
| subbands=False, | |
| istft_vits=False, | |
| is_onnx=False, | |
| **kwargs): | |
| super().__init__() | |
| self.n_vocab = n_vocab | |
| 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_spk_conditioned_encoder = kwargs.get("use_spk_conditioned_encoder", False) | |
| self.use_transformer_flows = kwargs.get("use_transformer_flows", False) | |
| self.transformer_flow_type = kwargs.get("transformer_flow_type", "mono_layer_post_residual") | |
| if self.use_transformer_flows: | |
| assert self.transformer_flow_type in AVAILABLE_FLOW_TYPES, f"transformer_flow_type must be one of {AVAILABLE_FLOW_TYPES}" | |
| self.use_sdp = use_sdp | |
| # self.use_duration_discriminator = kwargs.get("use_duration_discriminator", False) | |
| self.use_noise_scaled_mas = kwargs.get("use_noise_scaled_mas", False) | |
| self.mas_noise_scale_initial = kwargs.get("mas_noise_scale_initial", 0.01) | |
| self.noise_scale_delta = kwargs.get("noise_scale_delta", 2e-6) | |
| self.current_mas_noise_scale = self.mas_noise_scale_initial | |
| if self.use_spk_conditioned_encoder and gin_channels > 0: | |
| self.enc_gin_channels = gin_channels | |
| else: | |
| self.enc_gin_channels = 0 | |
| self.enc_p = TextEncoder(n_vocab, | |
| inter_channels, | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| p_dropout, | |
| gin_channels=self.enc_gin_channels) | |
| if mb_istft_vits == True: | |
| print('Multi-band iSTFT VITS2') | |
| 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, is_onnx=is_onnx) | |
| elif ms_istft_vits == True: | |
| print('Multi-stream iSTFT VITS2') | |
| 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, is_onnx=is_onnx) | |
| elif istft_vits == True: | |
| print('iSTFT-VITS2') | |
| 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, is_onnx=is_onnx) | |
| else: | |
| print('No iSTFT arguments found in json file') | |
| self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, | |
| upsample_rates, | |
| upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels) # vits 2 | |
| 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.flow = ResidualCouplingTransformersBlock( | |
| inter_channels, | |
| hidden_channels, | |
| 5, | |
| 1, | |
| 4, | |
| gin_channels=gin_channels, | |
| use_transformer_flows=self.use_transformer_flows, | |
| transformer_flow_type=self.transformer_flow_type | |
| ) | |
| if use_sdp: | |
| self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels) | |
| else: | |
| self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels) | |
| self.emb_g = nn.Embedding(n_speakers, gin_channels) | |
| def forward(self, x, x_lengths, y, y_lengths, sid=None): | |
| # x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths) | |
| g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] | |
| x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, g=g) # vits2? | |
| z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) | |
| z_p = self.flow(z, y_mask, g=g) | |
| with torch.no_grad(): | |
| # negative cross-entropy | |
| s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t] | |
| neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s] | |
| 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] | |
| 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] | |
| neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s] | |
| neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4 | |
| if self.use_noise_scaled_mas: | |
| epsilon = torch.std(neg_cent) * torch.randn_like(neg_cent) * self.current_mas_noise_scale | |
| neg_cent = neg_cent + epsilon | |
| attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1) | |
| attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach() | |
| w = attn.sum(2) | |
| if self.use_sdp: | |
| l_length = self.dp(x, x_mask, w, g=g) | |
| l_length = l_length / torch.sum(x_mask) | |
| logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=1.) | |
| logw_ = torch.log(w + 1e-6) * x_mask | |
| else: | |
| logw_ = torch.log(w + 1e-6) * x_mask | |
| logw = self.dp(x, x_mask, g=g) | |
| l_length = torch.sum((logw - logw_) ** 2, [1, 2]) / torch.sum(x_mask) # for averaging | |
| # expand prior | |
| m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) | |
| logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) | |
| z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size) | |
| o, o_mb = self.dec(z_slice, g=g) | |
| return o, o_mb, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q), (x, logw, logw_) | |
| def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None): | |
| g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] | |
| x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, g=g) | |
| if self.use_sdp: | |
| logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) | |
| else: | |
| logw = self.dp(x, x_mask, g=g) | |
| 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) | |
| o, o_mb = self.dec((z * y_mask)[:, :, :max_len], g=g) | |
| return o, o_mb, attn, y_mask, (z, z_p, m_p, logs_p) | |
| #''' | |
| ## currently vits-2 is not capable of voice conversion | |
| # comment - choihkk : Assuming the use of the ResidualCouplingTransformersLayer2 module, it seems that voice conversion is possible | |
| def voice_conversion(self, y, y_lengths, sid_src, sid_tgt): | |
| assert self.n_speakers > 0, "n_speakers have to be larger than 0." | |
| g_src = self.emb_g(sid_src).unsqueeze(-1) | |
| g_tgt = self.emb_g(sid_tgt).unsqueeze(-1) | |
| z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src) | |
| z_p = self.flow(z, y_mask, g=g_src) | |
| z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True) | |
| o_hat, o_hat_mb = self.dec(z_hat * y_mask, g=g_tgt) | |
| return o_hat, o_hat_mb, y_mask, (z, z_p, z_hat) | |
| #''' | |