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| from typing import Tuple | |
| import torch | |
| import torch.nn as nn | |
| from torch.nn import functional as F | |
| from modules.commons import sequence_mask | |
| class InterpolateRegulator(nn.Module): | |
| def __init__( | |
| self, | |
| channels: int, | |
| sampling_ratios: Tuple, | |
| is_discrete: bool = False, | |
| codebook_size: int = 1024, # for discrete only | |
| out_channels: int = None, | |
| groups: int = 1, | |
| token_dropout_prob: float = 0.5, # randomly drop out input tokens | |
| token_dropout_range: float = 0.5, # randomly drop out input tokens | |
| n_codebooks: int = 1, # number of codebooks | |
| quantizer_dropout: float = 0.0, # dropout for quantizer | |
| f0_condition: bool = False, | |
| n_f0_bins: int = 512, | |
| ): | |
| super().__init__() | |
| self.sampling_ratios = sampling_ratios | |
| out_channels = out_channels or channels | |
| model = nn.ModuleList([]) | |
| if len(sampling_ratios) > 0: | |
| for _ in sampling_ratios: | |
| module = nn.Conv1d(channels, channels, 3, 1, 1) | |
| norm = nn.GroupNorm(groups, channels) | |
| act = nn.Mish() | |
| model.extend([module, norm, act]) | |
| model.append( | |
| nn.Conv1d(channels, out_channels, 1, 1) | |
| ) | |
| self.model = nn.Sequential(*model) | |
| self.embedding = nn.Embedding(codebook_size, channels) | |
| self.is_discrete = is_discrete | |
| self.mask_token = nn.Parameter(torch.zeros(1, channels)) | |
| self.n_codebooks = n_codebooks | |
| if n_codebooks > 1: | |
| self.extra_codebooks = nn.ModuleList([ | |
| nn.Embedding(codebook_size, channels) for _ in range(n_codebooks - 1) | |
| ]) | |
| self.token_dropout_prob = token_dropout_prob | |
| self.token_dropout_range = token_dropout_range | |
| self.quantizer_dropout = quantizer_dropout | |
| if f0_condition: | |
| self.f0_embedding = nn.Embedding(n_f0_bins, channels) | |
| self.f0_condition = f0_condition | |
| self.n_f0_bins = n_f0_bins | |
| self.f0_bins = torch.arange(2, 1024, 1024 // n_f0_bins) | |
| self.f0_mask = nn.Parameter(torch.zeros(1, channels)) | |
| else: | |
| self.f0_condition = False | |
| def forward(self, x, ylens=None, n_quantizers=None, f0=None): | |
| # apply token drop | |
| if self.training: | |
| n_quantizers = torch.ones((x.shape[0],)) * self.n_codebooks | |
| dropout = torch.randint(1, self.n_codebooks + 1, (x.shape[0],)) | |
| n_dropout = int(x.shape[0] * self.quantizer_dropout) | |
| n_quantizers[:n_dropout] = dropout[:n_dropout] | |
| n_quantizers = n_quantizers.to(x.device) | |
| # decide whether to drop for each sample in batch | |
| else: | |
| n_quantizers = torch.ones((x.shape[0],), device=x.device) * (self.n_codebooks if n_quantizers is None else n_quantizers) | |
| if self.is_discrete: | |
| if self.n_codebooks > 1: | |
| assert len(x.size()) == 3 | |
| x_emb = self.embedding(x[:, 0]) | |
| for i, emb in enumerate(self.extra_codebooks): | |
| x_emb = x_emb + (n_quantizers > i+1)[..., None, None] * emb(x[:, i+1]) | |
| x = x_emb | |
| elif self.n_codebooks == 1: | |
| if len(x.size()) == 2: | |
| x = self.embedding(x) | |
| else: | |
| x = self.embedding(x[:, 0]) | |
| # x in (B, T, D) | |
| mask = sequence_mask(ylens).unsqueeze(-1) | |
| x = F.interpolate(x.transpose(1, 2).contiguous(), size=ylens.max(), mode='nearest') | |
| if self.f0_condition: | |
| if f0 is None: | |
| x = x + self.f0_mask.unsqueeze(-1) | |
| else: | |
| quantized_f0 = torch.bucketize(f0, self.f0_bins.to(f0.device)) # (N, T) | |
| if self.training: | |
| drop_f0 = torch.rand(quantized_f0.size(0)).to(f0.device) < self.quantizer_dropout | |
| else: | |
| drop_f0 = torch.zeros(quantized_f0.size(0)).to(f0.device).bool() | |
| f0_emb = self.f0_embedding(quantized_f0) | |
| f0_emb[drop_f0] = self.f0_mask | |
| f0_emb = F.interpolate(f0_emb.transpose(1, 2).contiguous(), size=ylens.max(), mode='nearest') | |
| x = x + f0_emb | |
| out = self.model(x).transpose(1, 2).contiguous() | |
| olens = ylens | |
| return out * mask, olens | |