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import torch
from torch import nn
from modules.commons.layers import LayerNorm
import torch.nn.functional as F
class DurationPredictor(torch.nn.Module):
def __init__(self, idim, n_layers=2, n_chans=384, kernel_size=3, dropout_rate=0.1, offset=1.0):
super(DurationPredictor, self).__init__()
self.offset = offset
self.conv = torch.nn.ModuleList()
self.kernel_size = kernel_size
for idx in range(n_layers):
in_chans = idim if idx == 0 else n_chans
self.conv += [torch.nn.Sequential(
torch.nn.Conv1d(in_chans, n_chans, kernel_size, stride=1, padding=kernel_size // 2),
torch.nn.ReLU(),
LayerNorm(n_chans, dim=1),
torch.nn.Dropout(dropout_rate)
)]
self.linear = nn.Sequential(torch.nn.Linear(n_chans, 1), nn.Softplus())
def forward(self, x, x_padding=None):
x = x.transpose(1, -1) # (B, idim, Tmax)
for f in self.conv:
x = f(x) # (B, C, Tmax)
if x_padding is not None:
x = x * (1 - x_padding.float())[:, None, :]
x = self.linear(x.transpose(1, -1)) # [B, T, C]
x = x * (1 - x_padding.float())[:, :, None] # (B, T, C)
x = x[..., 0] # (B, Tmax)
return x
class LengthRegulator(torch.nn.Module):
def __init__(self, pad_value=0.0):
super(LengthRegulator, self).__init__()
self.pad_value = pad_value
def forward(self, dur, dur_padding=None, alpha=1.0):
"""
Example (no batch dim version):
1. dur = [2,2,3]
2. token_idx = [[1],[2],[3]], dur_cumsum = [2,4,7], dur_cumsum_prev = [0,2,4]
3. token_mask = [[1,1,0,0,0,0,0],
[0,0,1,1,0,0,0],
[0,0,0,0,1,1,1]]
4. token_idx * token_mask = [[1,1,0,0,0,0,0],
[0,0,2,2,0,0,0],
[0,0,0,0,3,3,3]]
5. (token_idx * token_mask).sum(0) = [1,1,2,2,3,3,3]
:param dur: Batch of durations of each frame (B, T_txt)
:param dur_padding: Batch of padding of each frame (B, T_txt)
:param alpha: duration rescale coefficient
:return:
mel2ph (B, T_speech)
assert alpha > 0
"""
dur = torch.round(dur.float() * alpha).long()
if dur_padding is not None:
dur = dur * (1 - dur_padding.long())
token_idx = torch.arange(1, dur.shape[1] + 1)[None, :, None].to(dur.device)
dur_cumsum = torch.cumsum(dur, 1)
dur_cumsum_prev = F.pad(dur_cumsum, [1, -1], mode='constant', value=0)
pos_idx = torch.arange(dur.sum(-1).max())[None, None].to(dur.device)
token_mask = (pos_idx >= dur_cumsum_prev[:, :, None]) & (pos_idx < dur_cumsum[:, :, None])
mel2token = (token_idx * token_mask.long()).sum(1)
return mel2token
class PitchPredictor(torch.nn.Module):
def __init__(self, idim, n_layers=5, n_chans=384, odim=2, kernel_size=5, dropout_rate=0.1):
super(PitchPredictor, self).__init__()
self.conv = torch.nn.ModuleList()
self.kernel_size = kernel_size
for idx in range(n_layers):
in_chans = idim if idx == 0 else n_chans
self.conv += [torch.nn.Sequential(
torch.nn.Conv1d(in_chans, n_chans, kernel_size, padding=kernel_size // 2),
torch.nn.ReLU(),
LayerNorm(n_chans, dim=1),
torch.nn.Dropout(dropout_rate)
)]
self.linear = torch.nn.Linear(n_chans, odim)
def forward(self, x):
"""
:param x: [B, T, H]
:return: [B, T, H]
"""
x = x.transpose(1, -1) # (B, idim, Tmax)
for f in self.conv:
x = f(x) # (B, C, Tmax)
x = self.linear(x.transpose(1, -1)) # (B, Tmax, H)
return x
class EnergyPredictor(PitchPredictor):
pass
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