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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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from ttv_v1 import modules |
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import attentions |
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from torch.nn import Conv1d, ConvTranspose1d |
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from torch.nn.utils import weight_norm, remove_weight_norm |
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from commons import init_weights |
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import typing as tp |
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import transformers |
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import math |
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from ttv_v1.styleencoder import StyleEncoder |
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import commons |
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from ttv_v1.modules import WN |
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def get_2d_padding(kernel_size: tp.Tuple[int, int], dilation: tp.Tuple[int, int] = (1, 1)): |
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return (((kernel_size[0] - 1) * dilation[0]) // 2, ((kernel_size[1] - 1) * dilation[1]) // 2) |
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class Wav2vec2(torch.nn.Module): |
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def __init__(self, layer=7): |
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|
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"""we use the intermediate features of xls-r-300m. |
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More specifically, we used the output from the 12th layer of the 24-layer transformer encoder. |
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""" |
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super().__init__() |
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self.wav2vec2 = transformers.Wav2Vec2ForPreTraining.from_pretrained("facebook/mms-300m") |
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for param in self.wav2vec2.parameters(): |
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param.requires_grad = False |
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param.grad = None |
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self.wav2vec2.eval() |
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self.feature_layer = layer |
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@torch.no_grad() |
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def forward(self, x): |
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""" |
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Args: |
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x: torch.Tensor of shape (B x t) |
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Returns: |
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y: torch.Tensor of shape(B x C x t) |
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""" |
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outputs = self.wav2vec2(x.squeeze(1), output_hidden_states=True) |
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y = outputs.hidden_states[self.feature_layer] |
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y = y.permute((0, 2, 1)) |
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return y |
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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.cond = nn.Conv1d(256, hidden_channels, 1) |
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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.encoder2 = 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, g): |
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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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x = x + self.cond(g) |
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x = self.encoder2(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_Transformer(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=3, |
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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.cond_block = torch.nn.Sequential(torch.nn.Linear(gin_channels, 4 * hidden_channels), |
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nn.SiLU(), torch.nn.Linear(4 * hidden_channels, hidden_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_Transformer_simple(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True, attention_head=4)) |
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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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g = self.cond_block(g.squeeze(2)) |
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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_mask, g=None): |
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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 |
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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 W2VDecoder(nn.Module): |
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def __init__(self, |
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in_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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output_size=1024, |
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gin_channels=0, |
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p_dropout=0): |
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super().__init__() |
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self.in_channels = in_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.p_dropout = p_dropout |
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self.pre = nn.Conv1d(in_channels, hidden_channels, 1) |
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self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, p_dropout=p_dropout) |
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self.proj = nn.Conv1d(hidden_channels, output_size, 1) |
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def forward(self, x, x_mask, g=None): |
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x = self.pre(x * x_mask) * x_mask |
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x = self.enc(x, x_mask, g=g) |
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x = self.proj(x) * x_mask |
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return x |
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class PitchPredictor(nn.Module): |
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def __init__(self): |
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super().__init__() |
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resblock_kernel_sizes = [3,5,7] |
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upsample_rates = [2,2] |
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initial_channel = 1024 |
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upsample_initial_channel = 256 |
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upsample_kernel_sizes = [4,4] |
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resblock_dilation_sizes = [[1,3,5], [1,3,5], [1,3,5]] |
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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 |
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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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self.cond = Conv1d(256, upsample_initial_channel, 1) |
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def forward(self, x, g): |
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x = self.conv_pre(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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return x |
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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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def __init__(self, |
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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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gin_channels=256, |
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prosody_size=20, |
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cfg=False, |
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**kwargs): |
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super().__init__() |
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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.segment_size = segment_size |
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self.mel_size = prosody_size |
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self.enc_q = PosteriorEncoder(1024, inter_channels, hidden_channels, 5, 1, 16, gin_channels=256) |
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self.enc_p = TextEncoder(178, out_channels=inter_channels, hidden_channels=inter_channels, filter_channels=inter_channels*4, |
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n_heads=4, n_layers=3, kernel_size=9, p_dropout=0.2) |
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self.flow = ResidualCouplingBlock_Transformer(inter_channels, hidden_channels, 5, 1, 3, gin_channels=256) |
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self.w2v_decoder = W2VDecoder(inter_channels, inter_channels*2, 5, 1, 8, output_size=1024, p_dropout=0.1, gin_channels=256) |
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self.emb_g = StyleEncoder(in_dim=80, hidden_dim=256, out_dim=256) |
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self.dp = StochasticDurationPredictor(inter_channels, inter_channels, 3, 0.5, 4, gin_channels=256) |
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self.pp = PitchPredictor() |
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self.phoneme_classifier = Conv1d(inter_channels, 178, 1, bias=False) |
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@torch.no_grad() |
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def infer(self, x, x_lengths, y_mel, y_length, noise_scale=1, noise_scale_w=1, length_scale=1): |
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y_mask = torch.unsqueeze(commons.sequence_mask(y_length, y_mel.size(2)), 1).to(y_mel.dtype) |
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g = self.emb_g(y_mel, y_mask).unsqueeze(-1) |
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x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, g=g) |
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logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) |
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w = torch.exp(logw) * x_mask * length_scale |
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w_ceil = torch.ceil(w) |
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y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long() |
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y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype) |
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attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1) |
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attn = commons.generate_path(w_ceil, attn_mask) |
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m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) |
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logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) |
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z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale |
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z = self.flow(z_p, y_mask, g=g, reverse=True) |
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w2v = self.w2v_decoder(z, y_mask, g=g) |
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pitch = self.pp(w2v, g) |
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return w2v, pitch |
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@torch.no_grad() |
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def infer_noise_control(self, x, x_lengths, y_mel, y_length, noise_scale=0.333, noise_scale_w=1, length_scale=1, denoise_ratio = 0): |
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y_mask = torch.unsqueeze(commons.sequence_mask(y_length, y_mel.size(2)), 1).to(y_mel.dtype) |
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g = self.emb_g(y_mel, y_mask).unsqueeze(-1) |
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g_org, g_denoise = g[:1, :, :], g[1:, :, :] |
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g = (1-denoise_ratio)*g_org + denoise_ratio*g_denoise |
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x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, g=g) |
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logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) |
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w = torch.exp(logw) * x_mask * length_scale |
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w_ceil = torch.ceil(w) |
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y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long() |
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y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype) |
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attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1) |
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attn = commons.generate_path(w_ceil, attn_mask) |
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m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) |
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logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) |
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z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale |
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z = self.flow(z_p, y_mask, g=g, reverse=True) |
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w2v = self.w2v_decoder(z, y_mask, g=g) |
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pitch = self.pp(w2v, g) |
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return w2v, pitch |
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