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from typing import List, Optional, Tuple
import torch
from torch import Tensor
from torch.nn import Module
import torch.nn.functional as F
from models.config import AcousticModelConfigType
from .variance_predictor import VariancePredictor
class DurationAdaptor(Module):
"""DurationAdaptor is a module that adapts the duration of the input sequence.
Args:
model_config (AcousticModelConfigType): Configuration object containing model parameters.
"""
def __init__(
self,
model_config: AcousticModelConfigType,
):
super().__init__()
# Initialize the duration predictor
self.duration_predictor = VariancePredictor(
channels_in=model_config.encoder.n_hidden,
channels=model_config.variance_adaptor.n_hidden,
channels_out=1,
kernel_size=model_config.variance_adaptor.kernel_size,
p_dropout=model_config.variance_adaptor.p_dropout,
)
@staticmethod
def convert_pad_shape(pad_shape: List[List[int]]) -> List[int]:
r"""Convert the padding shape from a list of lists to a flat list.
Args:
pad_shape (List[List[int]]): Padding shape as a list of lists.
Returns:
List[int]: Padding shape as a flat list.
"""
pad_list = pad_shape[::-1]
return [item for sublist in pad_list for item in sublist]
@staticmethod
def generate_path(duration: Tensor, mask: Tensor) -> Tensor:
r"""Generate a path based on the duration and mask.
Args:
duration (Tensor): Duration tensor.
mask (Tensor): Mask tensor.
Returns:
Tensor: Path tensor.
Shapes:
- duration: :math:`[B, T_en]`
- mask: :math:'[B, T_en, T_de]`
- path: :math:`[B, T_en, T_de]`
"""
b, t_x, t_y = mask.shape
cum_duration = torch.cumsum(duration, 1)
cum_duration_flat = cum_duration.view(b * t_x)
path = DurationAdaptor.sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
path = path.view(b, t_x, t_y)
pad_shape = DurationAdaptor.convert_pad_shape([[0, 0], [1, 0], [0, 0]])
path = path - F.pad(path, pad_shape)[:, :-1]
path = path * mask
return path
# from https://gist.github.com/jihunchoi/f1434a77df9db1bb337417854b398df1
@staticmethod
def sequence_mask(sequence_length: Tensor, max_len: Optional[int] = None) -> Tensor:
"""Create a sequence mask for filtering padding in a sequence tensor.
Args:
sequence_length (torch.Tensor): Sequence lengths.
max_len (int, Optional): Maximum sequence length. Defaults to None.
Returns:
torch.Tensor: Sequence mask.
Shapes:
- mask: :math:`[B, T_max]`
"""
if max_len is None:
max_len = int(sequence_length.max())
seq_range = torch.arange(
max_len,
dtype=sequence_length.dtype,
device=sequence_length.device,
)
# B x T_max
return seq_range.unsqueeze(0) < sequence_length.unsqueeze(1)
@staticmethod
def generate_attn(
dr: Tensor,
x_mask: Tensor,
y_mask: Optional[Tensor] = None,
) -> Tensor:
"""Generate an attention mask from the linear scale durations.
Args:
dr (Tensor): Linear scale durations.
x_mask (Tensor): Mask for the input (character) sequence.
y_mask (Tensor): Mask for the output (spectrogram) sequence. Compute it from the predicted durations
if None. Defaults to None.
Shapes
- dr: :math:`(B, T_{en})`
- x_mask: :math:`(B, T_{en})`
- y_mask: :math:`(B, T_{de})`
"""
# compute decode mask from the durations
if y_mask is None:
y_lengths = dr.sum(1).long()
y_lengths[y_lengths < 1] = 1
sequence_mask = DurationAdaptor.sequence_mask(y_lengths, None)
y_mask = torch.unsqueeze(sequence_mask, 1).to(dr.dtype)
# compute the attention mask
attn_mask = torch.unsqueeze(x_mask, -1) * torch.unsqueeze(y_mask, 2)
attn = DurationAdaptor.generate_path(dr, attn_mask.squeeze(1)).to(dr.dtype)
return attn
def _expand_encoder_with_durations(
self,
encoder_output: Tensor,
duration_target: Tensor,
x_mask: Tensor,
mel_lens: Tensor,
) -> Tuple[Tensor, Tensor, Tensor]:
r"""Expand the encoder output with durations.
Args:
encoder_output (Tensor): Encoder output.
duration_target (Tensor): Target durations.
x_mask (Tensor): Mask for the input sequence.
mel_lens (Tensor): Lengths of the mel spectrograms.
Returns:
Tuple[Tensor, Tensor, Tensor]: Tuple containing the mask for the output sequence, the attention mask, and the expanded encoder output.
"""
y_mask = torch.unsqueeze(
DurationAdaptor.sequence_mask(mel_lens, None),
1,
).to(encoder_output.dtype)
attn = self.generate_attn(duration_target, x_mask, y_mask)
encoder_output_ex = torch.einsum(
"kmn, kmj -> kjn",
[attn.float(), encoder_output],
)
return y_mask, attn, encoder_output_ex
def forward_train(
self,
encoder_output: Tensor,
encoder_output_res: Tensor,
duration_target: Tensor,
src_mask: Tensor,
mel_lens: Tensor,
):
r"""Forward pass of the DurationAdaptor during training.
Args:
encoder_output (Tensor): Encoder output.
encoder_output_res (Tensor): Encoder output.
duration_target (Tensor): Target durations.
src_mask (Tensor): Source mask.
mel_lens (Tensor): Lengths of the mel spectrograms.
Returns:
Tuple: Tuple containing the predicted alignments, log durations, mask for the output sequence, expanded encoder output, and the transposed attention mask.
"""
log_duration_pred = self.duration_predictor.forward(
x=encoder_output_res.detach(),
mask=src_mask,
) # [B, C_hidden, T_src] -> [B, T_src]
y_mask, attn, encoder_output_dr = self._expand_encoder_with_durations(
encoder_output,
duration_target,
x_mask=~src_mask[:, None],
mel_lens=mel_lens,
)
duration_target = torch.log(duration_target + 1)
duration_pred = torch.exp(log_duration_pred) - 1
alignments_duration_pred = self.generate_attn(
duration_pred,
src_mask.unsqueeze(1),
y_mask,
) # [B, T_max, T_max2']
return (
alignments_duration_pred,
log_duration_pred,
encoder_output_dr,
attn.transpose(1, 2),
)
def forward(self, encoder_output: Tensor, src_mask: Tensor, d_control: float = 1.0):
r"""Forward pass of the DurationAdaptor.
Args:
encoder_output (Tensor): Encoder output.
src_mask (Tensor): Source mask.
d_control (float): Duration control. Defaults to 1.0.
Returns:
Tuple: Tuple containing the expanded encoder output, log durations, predicted durations, mask for the output sequence, and the attention mask.
"""
log_duration_pred = self.duration_predictor(
x=encoder_output.detach(),
mask=src_mask,
) # [B, C_hidden, T_src] -> [B, T_src]
duration_pred = (
(torch.exp(log_duration_pred) - 1) * (~src_mask) * d_control
) # -> [B, T_src]
# duration_pred[duration_pred < 1] = 1.0 # -> [B, T_src]
duration_pred = torch.where(
duration_pred < 1,
torch.ones_like(duration_pred),
duration_pred,
) # -> [B, T_src]
duration_pred = torch.round(duration_pred) # -> [B, T_src]
mel_lens = duration_pred.sum(1) # -> [B,]
_, attn, encoder_output_dr = self._expand_encoder_with_durations(
encoder_output,
duration_pred.squeeze(1),
~src_mask[:, None],
mel_lens,
)
return (
log_duration_pred,
encoder_output_dr,
duration_pred,
attn.transpose(1, 2),
)
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