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| from typing import Tuple | |
| 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, | |
| ): | |
| 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 | |
| def forward(self, x, ylens=None): | |
| if self.is_discrete: | |
| x = self.embedding(x) | |
| # x in (B, T, D) | |
| mask = sequence_mask(ylens).unsqueeze(-1) | |
| x = F.interpolate(x.transpose(1, 2).contiguous(), size=ylens.max(), mode='nearest') | |
| out = self.model(x).transpose(1, 2).contiguous() | |
| olens = ylens | |
| return out * mask, olens | |