#!/usr/bin/env python3
# Copyright 2020 Tomoki Hayashi
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Variance predictor related modules."""
import torch
from typeguard import typechecked
from espnet.nets.pytorch_backend.transformer.layer_norm import LayerNorm
[docs]class VariancePredictor(torch.nn.Module):
"""Variance predictor module.
This is a module of variacne predictor described in `FastSpeech 2:
Fast and High-Quality End-to-End Text to Speech`_.
.. _`FastSpeech 2: Fast and High-Quality End-to-End Text to Speech`:
https://arxiv.org/abs/2006.04558
"""
@typechecked
def __init__(
self,
idim: int,
n_layers: int = 2,
n_chans: int = 384,
kernel_size: int = 3,
bias: bool = True,
dropout_rate: float = 0.5,
):
"""Initilize duration predictor module.
Args:
idim (int): Input dimension.
n_layers (int): Number of convolutional layers.
n_chans (int): Number of channels of convolutional layers.
kernel_size (int): Kernel size of convolutional layers.
dropout_rate (float): Dropout rate.
"""
super().__init__()
self.conv = torch.nn.ModuleList()
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 - 1) // 2,
bias=bias,
),
torch.nn.ReLU(),
LayerNorm(n_chans, dim=1),
torch.nn.Dropout(dropout_rate),
)
]
self.linear = torch.nn.Linear(n_chans, 1)
[docs] def forward(self, xs: torch.Tensor, x_masks: torch.Tensor = None) -> torch.Tensor:
"""Calculate forward propagation.
Args:
xs (Tensor): Batch of input sequences (B, Tmax, idim).
x_masks (ByteTensor): Batch of masks indicating padded part (B, Tmax).
Returns:
Tensor: Batch of predicted sequences (B, Tmax, 1).
"""
xs = xs.transpose(1, -1) # (B, idim, Tmax)
for f in self.conv:
xs = f(xs) # (B, C, Tmax)
xs = self.linear(xs.transpose(1, 2)) # (B, Tmax, 1)
if x_masks is not None:
xs = xs.masked_fill(x_masks, 0.0)
return xs