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from typing import Tuple
import torch
from torch import nn
from torch.nn import Module
import torch.nn.functional as F
from models.config import (
AcousticModelConfigType,
PreprocessingConfig,
)
from models.helpers import tools
from models.tts.delightful_tts.constants import LEAKY_RELU_SLOPE
from models.tts.delightful_tts.conv_blocks import CoordConv1d
class ReferenceEncoder(Module):
r"""A class to define the reference encoder.
Similar to Tacotron model, the reference encoder is used to extract the high-level features from the reference
It consists of a number of convolutional blocks (`CoordConv1d` for the first one and `nn.Conv1d` for the rest),
then followed by instance normalization and GRU layers.
The `CoordConv1d` at the first layer to better preserve positional information, paper:
[Robust and fine-grained prosody control of end-to-end speech synthesis](https://arxiv.org/pdf/1811.02122.pdf)
Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence.
Args:
preprocess_config (PreprocessingConfig): Configuration object with preprocessing parameters.
model_config (AcousticModelConfigType): Configuration object with acoustic model parameters.
Returns:
Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: A tuple containing three tensors. _First_: The sequence tensor
produced by the last GRU layer after padding has been removed. _Second_: The GRU's final hidden state tensor.
_Third_: The mask tensor, which has the same shape as x, and contains `True` at positions where the input x
has been masked.
"""
def __init__(
self,
preprocess_config: PreprocessingConfig,
model_config: AcousticModelConfigType,
):
super().__init__()
n_mel_channels = preprocess_config.stft.n_mel_channels
ref_enc_filters = model_config.reference_encoder.ref_enc_filters
ref_enc_size = model_config.reference_encoder.ref_enc_size
ref_enc_strides = model_config.reference_encoder.ref_enc_strides
ref_enc_gru_size = model_config.reference_encoder.ref_enc_gru_size
self.n_mel_channels = n_mel_channels
K = len(ref_enc_filters)
filters = [self.n_mel_channels, *ref_enc_filters]
strides = [1, *ref_enc_strides]
# Use CoordConv1d at the first layer to better preserve positional information: https://arxiv.org/pdf/1811.02122.pdf
convs = [
CoordConv1d(
in_channels=filters[0],
out_channels=filters[0 + 1],
kernel_size=ref_enc_size,
stride=strides[0],
padding=ref_enc_size // 2,
with_r=True,
),
*[
nn.Conv1d(
in_channels=filters[i],
out_channels=filters[i + 1],
kernel_size=ref_enc_size,
stride=strides[i],
padding=ref_enc_size // 2,
)
for i in range(1, K)
],
]
# Define convolution layers (ModuleList)
self.convs = nn.ModuleList(convs)
self.norms = nn.ModuleList(
[
nn.InstanceNorm1d(num_features=ref_enc_filters[i], affine=True)
for i in range(K)
],
)
# Define GRU layer
self.gru = nn.GRU(
input_size=ref_enc_filters[-1],
hidden_size=ref_enc_gru_size,
batch_first=True,
)
def forward(
self,
x: torch.Tensor,
mel_lens: torch.Tensor,
leaky_relu_slope: float = LEAKY_RELU_SLOPE,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
r"""Forward pass of the ReferenceEncoder.
Args:
x (torch.Tensor): A 3-dimensional tensor containing the input sequences, its size is [N, n_mels, timesteps].
mel_lens (torch.Tensor): A 1-dimensional tensor containing the lengths of each sequence in x. Its length is N.
leaky_relu_slope (float): The slope of the leaky relu function.
Returns:
Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: A tuple containing three tensors. _First_: The sequence tensor
produced by the last GRU layer after padding has been removed. _Second_: The GRU's final hidden state tensor.
_Third_: The mask tensor, which has the same shape as x, and contains `True` at positions where the input x
has been masked.
"""
mel_masks = tools.get_mask_from_lengths(mel_lens).unsqueeze(1)
mel_masks = mel_masks.to(x.device)
x = x.masked_fill(mel_masks, 0)
for conv, norm in zip(self.convs, self.norms):
x = x.float()
x = conv(x)
x = F.leaky_relu(x, leaky_relu_slope) # [N, 128, Ty//2^K, n_mels//2^K]
x = norm(x)
for _ in range(2):
mel_lens = tools.stride_lens_downsampling(mel_lens)
mel_masks = tools.get_mask_from_lengths(mel_lens)
x = x.masked_fill(mel_masks.unsqueeze(1), 0)
x = x.permute((0, 2, 1))
packed_sequence = torch.nn.utils.rnn.pack_padded_sequence(
x,
lengths=mel_lens.cpu().int(),
batch_first=True,
enforce_sorted=False,
)
self.gru.flatten_parameters()
# memory --- [N, Ty, E//2], out --- [1, N, E//2]
out, memory = self.gru(packed_sequence)
out, _ = torch.nn.utils.rnn.pad_packed_sequence(out, batch_first=True)
return out, memory, mel_masks
def calculate_channels(
self,
L: int,
kernel_size: int,
stride: int,
pad: int,
n_convs: int,
) -> int:
r"""Calculate the number of channels after applying convolutions.
Args:
L (int): The original size.
kernel_size (int): The kernel size used in the convolutions.
stride (int): The stride used in the convolutions.
pad (int): The padding used in the convolutions.
n_convs (int): The number of convolutions.
Returns:
int: The size after the convolutions.
"""
# Loop through each convolution
for _ in range(n_convs):
# Calculate the size after each convolution
L = (L - kernel_size + 2 * pad) // stride + 1
return L
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