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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
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