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from typing import Tuple
import torch.nn as nn
import torch
from torch.nn import functional as F
from VietTTS.utils.mask import make_pad_mask
class InterpolateRegulator(nn.Module):
def __init__(
self,
channels: int,
sampling_ratios: Tuple,
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)
def forward(self, x, ylens=None):
# x in (B, T, D)
mask = (~make_pad_mask(ylens)).to(x).unsqueeze(-1)
x = F.interpolate(x.transpose(1, 2).contiguous(), size=ylens.max(), mode='linear')
out = self.model(x).transpose(1, 2).contiguous()
olens = ylens
return out * mask, olens
def inference(self, x1, x2, mel_len1, mel_len2, input_frame_rate=50):
# in inference mode, interploate prompt token and token(head/mid/tail) seprately, so we can get a clear separation point of mel
# x in (B, T, D)
if x2.shape[1] > 40:
x2_head = F.interpolate(x2[:, :20].transpose(1, 2).contiguous(), size=int(20 / input_frame_rate * 22050 / 256), mode='linear')
x2_mid = F.interpolate(x2[:, 20:-20].transpose(1, 2).contiguous(), size=mel_len2 - int(20 / input_frame_rate * 22050 / 256) * 2,
mode='linear')
x2_tail = F.interpolate(x2[:, -20:].transpose(1, 2).contiguous(), size=int(20 / input_frame_rate * 22050 / 256), mode='linear')
x2 = torch.concat([x2_head, x2_mid, x2_tail], dim=2)
else:
x2 = F.interpolate(x2.transpose(1, 2).contiguous(), size=mel_len2, mode='linear')
if x1.shape[1] != 0:
x1 = F.interpolate(x1.transpose(1, 2).contiguous(), size=mel_len1, mode='linear')
x = torch.concat([x1, x2], dim=2)
else:
x = x2
out = self.model(x).transpose(1, 2).contiguous()
return out, mel_len1 + mel_len2
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