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from typing import Tuple
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import torch.nn as nn
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from torch.nn import functional as F
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from modules.commons import sequence_mask
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class InterpolateRegulator(nn.Module):
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def __init__(
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self,
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channels: int,
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sampling_ratios: Tuple,
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is_discrete: bool = False,
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codebook_size: int = 1024,
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out_channels: int = None,
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groups: int = 1,
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):
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super().__init__()
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self.sampling_ratios = sampling_ratios
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out_channels = out_channels or channels
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model = nn.ModuleList([])
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if len(sampling_ratios) > 0:
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for _ in sampling_ratios:
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module = nn.Conv1d(channels, channels, 3, 1, 1)
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norm = nn.GroupNorm(groups, channels)
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act = nn.Mish()
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model.extend([module, norm, act])
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model.append(
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nn.Conv1d(channels, out_channels, 1, 1)
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)
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self.model = nn.Sequential(*model)
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self.embedding = nn.Embedding(codebook_size, channels)
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self.is_discrete = is_discrete
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def forward(self, x, ylens=None):
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if self.is_discrete:
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x = self.embedding(x)
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mask = sequence_mask(ylens).unsqueeze(-1)
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x = F.interpolate(x.transpose(1, 2).contiguous(), size=ylens.max(), mode='nearest')
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out = self.model(x).transpose(1, 2).contiguous()
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olens = ylens
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return out * mask, olens
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