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import torch
import torch.nn.functional as F
from torch import nn
class FFTransformer(nn.Module):
def __init__(self, in_out_channels, num_heads, hidden_channels_ffn=1024, kernel_size_fft=3, dropout_p=0.1):
super().__init__()
self.self_attn = nn.MultiheadAttention(in_out_channels, num_heads, dropout=dropout_p)
padding = (kernel_size_fft - 1) // 2
self.conv1 = nn.Conv1d(in_out_channels, hidden_channels_ffn, kernel_size=kernel_size_fft, padding=padding)
self.conv2 = nn.Conv1d(hidden_channels_ffn, in_out_channels, kernel_size=kernel_size_fft, padding=padding)
self.norm1 = nn.LayerNorm(in_out_channels)
self.norm2 = nn.LayerNorm(in_out_channels)
self.dropout1 = nn.Dropout(dropout_p)
self.dropout2 = nn.Dropout(dropout_p)
def forward(self, src, src_mask=None, src_key_padding_mask=None):
"""😦 ugly looking with all the transposing"""
src = src.permute(2, 0, 1)
src2, enc_align = self.self_attn(src, src, src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)
src = src + self.dropout1(src2)
src = self.norm1(src + src2)
# T x B x D -> B x D x T
src = src.permute(1, 2, 0)
src2 = self.conv2(F.relu(self.conv1(src)))
src2 = self.dropout2(src2)
src = src + src2
src = src.transpose(1, 2)
src = self.norm2(src)
src = src.transpose(1, 2)
return src, enc_align
class FFTransformerBlock(nn.Module):
def __init__(self, in_out_channels, num_heads, hidden_channels_ffn, num_layers, dropout_p):
super().__init__()
self.fft_layers = nn.ModuleList(
[
FFTransformer(
in_out_channels=in_out_channels,
num_heads=num_heads,
hidden_channels_ffn=hidden_channels_ffn,
dropout_p=dropout_p,
)
for _ in range(num_layers)
]
)
def forward(self, x, mask=None, g=None): # pylint: disable=unused-argument
"""
TODO: handle multi-speaker
Shapes:
- x: :math:`[B, C, T]`
- mask: :math:`[B, 1, T] or [B, T]`
"""
if mask is not None and mask.ndim == 3:
mask = mask.squeeze(1)
# mask is negated, torch uses 1s and 0s reversely.
mask = ~mask.bool()
alignments = []
for layer in self.fft_layers:
x, align = layer(x, src_key_padding_mask=mask)
alignments.append(align.unsqueeze(1))
alignments = torch.cat(alignments, 1)
return x
class FFTDurationPredictor:
def __init__(
self, in_channels, hidden_channels, num_heads, num_layers, dropout_p=0.1, cond_channels=None
): # pylint: disable=unused-argument
self.fft = FFTransformerBlock(in_channels, num_heads, hidden_channels, num_layers, dropout_p)
self.proj = nn.Linear(in_channels, 1)
def forward(self, x, mask=None, g=None): # pylint: disable=unused-argument
"""
Shapes:
- x: :math:`[B, C, T]`
- mask: :math:`[B, 1, T]`
TODO: Handle the cond input
"""
x = self.fft(x, mask=mask)
x = self.proj(x)
return x
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