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import copy |
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import math |
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import torch |
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from torch import nn |
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from torch.nn import functional as F |
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import commons |
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import modules |
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import attentions |
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import monotonic_align |
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from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d |
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from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm |
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from commons import init_weights, get_padding |
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class StochasticDurationPredictor(nn.Module): |
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def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0): |
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super().__init__() |
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filter_channels = in_channels |
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self.in_channels = in_channels |
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self.filter_channels = filter_channels |
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self.kernel_size = kernel_size |
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self.p_dropout = p_dropout |
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self.n_flows = n_flows |
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self.gin_channels = gin_channels |
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self.log_flow = modules.Log() |
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self.flows = nn.ModuleList() |
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self.flows.append(modules.ElementwiseAffine(2)) |
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for i in range(n_flows): |
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self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)) |
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self.flows.append(modules.Flip()) |
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self.post_pre = nn.Conv1d(1, filter_channels, 1) |
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self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1) |
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self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout) |
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self.post_flows = nn.ModuleList() |
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self.post_flows.append(modules.ElementwiseAffine(2)) |
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for i in range(4): |
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self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)) |
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self.post_flows.append(modules.Flip()) |
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self.pre = nn.Conv1d(in_channels, filter_channels, 1) |
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self.proj = nn.Conv1d(filter_channels, filter_channels, 1) |
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self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout) |
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if gin_channels != 0: |
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self.cond = nn.Conv1d(gin_channels, filter_channels, 1) |
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def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0): |
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x = torch.detach(x) |
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x = self.pre(x) |
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if g is not None: |
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g = torch.detach(g) |
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x = x + self.cond(g) |
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x = self.convs(x, x_mask) |
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x = self.proj(x) * x_mask |
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if not reverse: |
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flows = self.flows |
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assert w is not None |
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logdet_tot_q = 0 |
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h_w = self.post_pre(w) |
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h_w = self.post_convs(h_w, x_mask) |
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h_w = self.post_proj(h_w) * x_mask |
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e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask |
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z_q = e_q |
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for flow in self.post_flows: |
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z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w)) |
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logdet_tot_q += logdet_q |
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z_u, z1 = torch.split(z_q, [1, 1], 1) |
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u = torch.sigmoid(z_u) * x_mask |
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z0 = (w - u) * x_mask |
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logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2]) |
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logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q |
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logdet_tot = 0 |
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z0, logdet = self.log_flow(z0, x_mask) |
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logdet_tot += logdet |
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z = torch.cat([z0, z1], 1) |
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for flow in flows: |
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z, logdet = flow(z, x_mask, g=x, reverse=reverse) |
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logdet_tot = logdet_tot + logdet |
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nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot |
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return nll + logq |
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else: |
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flows = list(reversed(self.flows)) |
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flows = flows[:-2] + [flows[-1]] |
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z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale |
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for flow in flows: |
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z = flow(z, x_mask, g=x, reverse=reverse) |
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z0, z1 = torch.split(z, [1, 1], 1) |
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logw = z0 |
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return logw |
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class DurationPredictor(nn.Module): |
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def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0): |
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super().__init__() |
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self.in_channels = in_channels |
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self.filter_channels = filter_channels |
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self.kernel_size = kernel_size |
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self.p_dropout = p_dropout |
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self.gin_channels = gin_channels |
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self.drop = nn.Dropout(p_dropout) |
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self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2) |
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self.norm_1 = modules.LayerNorm(filter_channels) |
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self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2) |
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self.norm_2 = modules.LayerNorm(filter_channels) |
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self.proj = nn.Conv1d(filter_channels, 1, 1) |
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if gin_channels != 0: |
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self.cond = nn.Conv1d(gin_channels, in_channels, 1) |
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def forward(self, x, x_mask, g=None): |
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x = torch.detach(x) |
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if g is not None: |
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g = torch.detach(g) |
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x = x + self.cond(g) |
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x = self.conv_1(x * x_mask) |
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x = torch.relu(x) |
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x = self.norm_1(x) |
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x = self.drop(x) |
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x = self.conv_2(x * x_mask) |
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x = torch.relu(x) |
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x = self.norm_2(x) |
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x = self.drop(x) |
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x = self.proj(x * x_mask) |
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return x * x_mask |
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class TextEncoder(nn.Module): |
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def __init__(self, |
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n_vocab, |
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out_channels, |
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hidden_channels, |
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filter_channels, |
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n_heads, |
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n_layers, |
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kernel_size, |
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p_dropout): |
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super().__init__() |
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self.n_vocab = n_vocab |
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self.out_channels = out_channels |
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self.hidden_channels = hidden_channels |
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self.filter_channels = filter_channels |
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self.n_heads = n_heads |
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self.n_layers = n_layers |
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self.kernel_size = kernel_size |
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self.p_dropout = p_dropout |
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self.emb = nn.Embedding(n_vocab, hidden_channels) |
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nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5) |
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self.encoder = attentions.Encoder( |
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hidden_channels, |
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filter_channels, |
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n_heads, |
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n_layers, |
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kernel_size, |
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p_dropout) |
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self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1) |
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def forward(self, x, x_lengths): |
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x = self.emb(x) * math.sqrt(self.hidden_channels) |
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x = torch.transpose(x, 1, -1) |
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x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) |
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x = self.encoder(x * x_mask, x_mask) |
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stats = self.proj(x) * x_mask |
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m, logs = torch.split(stats, self.out_channels, dim=1) |
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return x, m, logs, x_mask |
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class TextEncoder_lora(nn.Module): |
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def __init__(self, |
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n_vocab, |
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out_channels, |
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hidden_channels, |
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filter_channels, |
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n_heads, |
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n_layers, |
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kernel_size, |
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p_dropout): |
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super().__init__() |
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self.n_vocab = n_vocab |
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self.out_channels = out_channels |
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self.hidden_channels = hidden_channels |
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self.filter_channels = filter_channels |
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self.n_heads = n_heads |
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self.n_layers = n_layers |
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self.kernel_size = kernel_size |
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self.p_dropout = p_dropout |
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self.emb = nn.Embedding(n_vocab, hidden_channels, r=4) |
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nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5) |
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self.encoder = attentions.Encoder_lora( |
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hidden_channels, |
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filter_channels, |
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n_heads, |
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n_layers, |
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kernel_size, |
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p_dropout) |
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self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1) |
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def forward(self, x, x_lengths): |
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x = self.emb(x) * math.sqrt(self.hidden_channels) |
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x = torch.transpose(x, 1, -1) |
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x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) |
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x = self.encoder(x * x_mask, x_mask) |
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stats = self.proj(x) * x_mask |
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m, logs = torch.split(stats, self.out_channels, dim=1) |
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return x, m, logs, x_mask |
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class ResidualCouplingBlock(nn.Module): |
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def __init__(self, |
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channels, |
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hidden_channels, |
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kernel_size, |
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dilation_rate, |
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n_layers, |
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n_flows=4, |
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gin_channels=0): |
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super().__init__() |
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self.channels = channels |
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self.hidden_channels = hidden_channels |
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self.kernel_size = kernel_size |
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self.dilation_rate = dilation_rate |
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self.n_layers = n_layers |
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self.n_flows = n_flows |
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self.gin_channels = gin_channels |
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self.flows = nn.ModuleList() |
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for i in range(n_flows): |
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self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True)) |
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self.flows.append(modules.Flip()) |
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def forward(self, x, x_mask, g=None, reverse=False): |
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if not reverse: |
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for flow in self.flows: |
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x, _ = flow(x, x_mask, g=g, reverse=reverse) |
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else: |
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for flow in reversed(self.flows): |
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x = flow(x, x_mask, g=g, reverse=reverse) |
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return x |
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class PosteriorEncoder(nn.Module): |
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def __init__(self, |
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in_channels, |
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out_channels, |
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hidden_channels, |
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kernel_size, |
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dilation_rate, |
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n_layers, |
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gin_channels=0): |
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super().__init__() |
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self.in_channels = in_channels |
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self.out_channels = out_channels |
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self.hidden_channels = hidden_channels |
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self.kernel_size = kernel_size |
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self.dilation_rate = dilation_rate |
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self.n_layers = n_layers |
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self.gin_channels = gin_channels |
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self.pre = nn.Conv1d(in_channels, hidden_channels, 1) |
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self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels) |
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self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) |
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def forward(self, x, x_lengths, g=None): |
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x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) |
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x = self.pre(x) * x_mask |
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x = self.enc(x, x_mask, g=g) |
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stats = self.proj(x) * x_mask |
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m, logs = torch.split(stats, self.out_channels, dim=1) |
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z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask |
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return z, m, logs, x_mask |
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class Generator(torch.nn.Module): |
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def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0): |
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super(Generator, self).__init__() |
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self.num_kernels = len(resblock_kernel_sizes) |
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self.num_upsamples = len(upsample_rates) |
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self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3) |
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resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2 |
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self.ups = nn.ModuleList() |
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for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): |
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self.ups.append(weight_norm( |
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ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)), |
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k, u, padding=(k-u)//2))) |
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self.resblocks = nn.ModuleList() |
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for i in range(len(self.ups)): |
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ch = upsample_initial_channel//(2**(i+1)) |
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for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)): |
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self.resblocks.append(resblock(ch, k, d)) |
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self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) |
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self.ups.apply(init_weights) |
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if gin_channels != 0: |
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self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) |
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def forward(self, x, g=None): |
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x = self.conv_pre(x) |
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if g is not None: |
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x = x + self.cond(g) |
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for i in range(self.num_upsamples): |
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x = F.leaky_relu(x, modules.LRELU_SLOPE) |
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x = self.ups[i](x) |
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xs = None |
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for j in range(self.num_kernels): |
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if xs is None: |
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xs = self.resblocks[i*self.num_kernels+j](x) |
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else: |
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xs += self.resblocks[i*self.num_kernels+j](x) |
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x = xs / self.num_kernels |
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x = F.leaky_relu(x) |
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x = self.conv_post(x) |
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x = torch.tanh(x) |
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return x |
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def remove_weight_norm(self): |
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print('Removing weight norm...') |
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for l in self.ups: |
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remove_weight_norm(l) |
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for l in self.resblocks: |
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l.remove_weight_norm() |
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class DiscriminatorP(torch.nn.Module): |
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def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): |
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super(DiscriminatorP, self).__init__() |
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self.period = period |
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self.use_spectral_norm = use_spectral_norm |
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norm_f = weight_norm if use_spectral_norm == False else spectral_norm |
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self.convs = nn.ModuleList([ |
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norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), |
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norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), |
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norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), |
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norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), |
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norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))), |
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]) |
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self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) |
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def forward(self, x): |
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fmap = [] |
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b, c, t = x.shape |
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if t % self.period != 0: |
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n_pad = self.period - (t % self.period) |
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x = F.pad(x, (0, n_pad), "reflect") |
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t = t + n_pad |
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x = x.view(b, c, t // self.period, self.period) |
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for l in self.convs: |
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x = l(x) |
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x = F.leaky_relu(x, modules.LRELU_SLOPE) |
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fmap.append(x) |
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x = self.conv_post(x) |
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fmap.append(x) |
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x = torch.flatten(x, 1, -1) |
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return x, fmap |
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class DiscriminatorS(torch.nn.Module): |
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def __init__(self, use_spectral_norm=False): |
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super(DiscriminatorS, self).__init__() |
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norm_f = weight_norm if use_spectral_norm == False else spectral_norm |
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self.convs = nn.ModuleList([ |
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norm_f(Conv1d(1, 16, 15, 1, padding=7)), |
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norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)), |
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norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)), |
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norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)), |
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norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)), |
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norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), |
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]) |
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self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) |
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def forward(self, x): |
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fmap = [] |
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for l in self.convs: |
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x = l(x) |
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x = F.leaky_relu(x, modules.LRELU_SLOPE) |
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fmap.append(x) |
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x = self.conv_post(x) |
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fmap.append(x) |
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x = torch.flatten(x, 1, -1) |
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return x, fmap |
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class MultiPeriodDiscriminator(torch.nn.Module): |
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def __init__(self, use_spectral_norm=False): |
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super(MultiPeriodDiscriminator, self).__init__() |
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periods = [2,3,5,7,11] |
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discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] |
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discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods] |
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self.discriminators = nn.ModuleList(discs) |
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def forward(self, y, y_hat): |
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y_d_rs = [] |
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y_d_gs = [] |
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fmap_rs = [] |
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fmap_gs = [] |
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for i, d in enumerate(self.discriminators): |
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y_d_r, fmap_r = d(y) |
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y_d_g, fmap_g = d(y_hat) |
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y_d_rs.append(y_d_r) |
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y_d_gs.append(y_d_g) |
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fmap_rs.append(fmap_r) |
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fmap_gs.append(fmap_g) |
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return y_d_rs, y_d_gs, fmap_rs, fmap_gs |
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class SynthesizerTrn(nn.Module): |
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""" |
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Synthesizer for Training |
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""" |
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|
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def __init__(self, |
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n_vocab, |
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spec_channels, |
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segment_size, |
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inter_channels, |
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hidden_channels, |
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filter_channels, |
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n_heads, |
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n_layers, |
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kernel_size, |
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p_dropout, |
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resblock, |
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resblock_kernel_sizes, |
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resblock_dilation_sizes, |
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upsample_rates, |
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upsample_initial_channel, |
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upsample_kernel_sizes, |
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n_speakers=0, |
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gin_channels=0, |
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use_sdp=True, |
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**kwargs): |
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|
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super().__init__() |
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self.n_vocab = n_vocab |
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self.spec_channels = spec_channels |
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self.inter_channels = inter_channels |
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self.hidden_channels = hidden_channels |
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self.filter_channels = filter_channels |
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self.n_heads = n_heads |
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self.n_layers = n_layers |
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self.kernel_size = kernel_size |
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self.p_dropout = p_dropout |
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self.resblock = resblock |
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self.resblock_kernel_sizes = resblock_kernel_sizes |
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self.resblock_dilation_sizes = resblock_dilation_sizes |
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self.upsample_rates = upsample_rates |
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self.upsample_initial_channel = upsample_initial_channel |
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self.upsample_kernel_sizes = upsample_kernel_sizes |
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self.segment_size = segment_size |
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self.n_speakers = n_speakers |
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self.gin_channels = gin_channels |
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|
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self.use_sdp = use_sdp |
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|
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self.enc_p = TextEncoder(n_vocab, |
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inter_channels, |
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hidden_channels, |
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filter_channels, |
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n_heads, |
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n_layers, |
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kernel_size, |
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p_dropout) |
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self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels) |
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self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels) |
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self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels) |
|
|
|
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) |
|
|
|
if n_speakers >= 1: |
|
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) |
|
if self.n_speakers > 0: |
|
g = self.emb_g(sid).unsqueeze(-1) |
|
else: |
|
g = None |
|
|
|
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(): |
|
|
|
s_p_sq_r = torch.exp(-2 * logs_p) |
|
neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) |
|
neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), s_p_sq_r) |
|
neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) |
|
neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) |
|
neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4 |
|
|
|
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) |
|
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) |
|
|
|
|
|
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 = self.dec(z_slice, g=g) |
|
return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q) |
|
|
|
def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None): |
|
x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths) |
|
if self.n_speakers > 0: |
|
g = self.emb_g(sid).unsqueeze(-1) |
|
else: |
|
g = None |
|
|
|
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) |
|
logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) |
|
|
|
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 = self.dec((z * y_mask)[:,:,:max_len], g=g) |
|
return o, attn, y_mask, (z, z_p, m_p, logs_p) |
|
|
|
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 = self.dec(z_hat * y_mask, g=g_tgt) |
|
return o_hat, y_mask, (z, z_p, z_hat) |
|
|
|
|
|
class SynthesizerTrn_lora(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, |
|
n_speakers=0, |
|
gin_channels=0, |
|
use_sdp=True, |
|
**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.use_sdp = use_sdp |
|
|
|
self.enc_p = TextEncoder_lora(n_vocab, |
|
inter_channels, |
|
hidden_channels, |
|
filter_channels, |
|
n_heads, |
|
n_layers, |
|
kernel_size, |
|
p_dropout) |
|
self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels) |
|
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) |
|
|
|
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) |
|
|
|
if n_speakers >= 1: |
|
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) |
|
if self.n_speakers > 0: |
|
g = self.emb_g(sid).unsqueeze(-1) |
|
else: |
|
g = None |
|
|
|
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(): |
|
|
|
s_p_sq_r = torch.exp(-2 * logs_p) |
|
neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) |
|
neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), s_p_sq_r) |
|
neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) |
|
neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) |
|
neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4 |
|
|
|
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) |
|
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) |
|
|
|
|
|
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 = self.dec(z_slice, g=g) |
|
return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q) |
|
|
|
def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None): |
|
x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths) |
|
if self.n_speakers > 0: |
|
g = self.emb_g(sid).unsqueeze(-1) |
|
else: |
|
g = None |
|
|
|
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) |
|
logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) |
|
|
|
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 = self.dec((z * y_mask)[:,:,:max_len], g=g) |
|
return o, attn, y_mask, (z, z_p, m_p, logs_p) |
|
|
|
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 = self.dec(z_hat * y_mask, g=g_tgt) |
|
return o_hat, y_mask, (z, z_p, z_hat) |
