#!/usr/bin/env python3
# -*- coding: utf-8 -*-

# Copyright 2019 Tomoki Hayashi
#           2023 Jiatong SHi

# Adapted from espnet/espnet/net/pytorch_backend/duration_predictor.py
# Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)

"""Duration predictor related modules."""

import torch


class DurationPredictorLoss(torch.nn.Module):
    """Loss function module for duration predictor.

    The loss value is Calculated in log domain to make it Gaussian.

    """

    def __init__(self, offset=1.0, reduction="mean"):
        """Initilize duration predictor loss module.

        Args:
            offset (float, optional): Offset value to avoid nan in log domain.
            reduction (str): Reduction type in loss calculation.

        """
        super(DurationPredictorLoss, self).__init__()
        self.criterion = torch.nn.MSELoss(reduction=reduction)
        self.offset = offset

    def forward(self, outputs, targets):
        """Calculate forward propagation.

        Args:
            outputs (Tensor): Batch of prediction durations in log domain (B, T)
            targets (LongTensor): Batch of groundtruth durations in linear domain (B, T)

        Returns:
            Tensor: Mean squared error loss value.

        Note:
            `outputs` is in log domain but `targets` is in linear domain.

        """
        # NOTE: outputs is in log domain while targets in linear
        targets = torch.log(targets.float() + self.offset)
        loss = self.criterion(outputs, targets)

        return loss

b40eb1adc242a7c9c1d2161ab713b7a1ebe15df4
<mask token>


class DurationPredictorLoss(torch.nn.Module):
    <mask token>
    <mask token>

    def forward(self, outputs, targets):
        """Calculate forward propagation.

        Args:
            outputs (Tensor): Batch of prediction durations in log domain (B, T)
            targets (LongTensor): Batch of groundtruth durations in linear domain (B, T)

        Returns:
            Tensor: Mean squared error loss value.

        Note:
            `outputs` is in log domain but `targets` is in linear domain.

        """
        targets = torch.log(targets.float() + self.offset)
        loss = self.criterion(outputs, targets)
        return loss

<mask token>


class DurationPredictorLoss(torch.nn.Module):
    <mask token>

    def __init__(self, offset=1.0, reduction='mean'):
        """Initilize duration predictor loss module.

        Args:
            offset (float, optional): Offset value to avoid nan in log domain.
            reduction (str): Reduction type in loss calculation.

        """
        super(DurationPredictorLoss, self).__init__()
        self.criterion = torch.nn.MSELoss(reduction=reduction)
        self.offset = offset

    def forward(self, outputs, targets):
        """Calculate forward propagation.

        Args:
            outputs (Tensor): Batch of prediction durations in log domain (B, T)
            targets (LongTensor): Batch of groundtruth durations in linear domain (B, T)

        Returns:
            Tensor: Mean squared error loss value.

        Note:
            `outputs` is in log domain but `targets` is in linear domain.

        """
        targets = torch.log(targets.float() + self.offset)
        loss = self.criterion(outputs, targets)
        return loss

<mask token>


class DurationPredictorLoss(torch.nn.Module):
    """Loss function module for duration predictor.

    The loss value is Calculated in log domain to make it Gaussian.

    """

    def __init__(self, offset=1.0, reduction='mean'):
        """Initilize duration predictor loss module.

        Args:
            offset (float, optional): Offset value to avoid nan in log domain.
            reduction (str): Reduction type in loss calculation.

        """
        super(DurationPredictorLoss, self).__init__()
        self.criterion = torch.nn.MSELoss(reduction=reduction)
        self.offset = offset

    def forward(self, outputs, targets):
        """Calculate forward propagation.

        Args:
            outputs (Tensor): Batch of prediction durations in log domain (B, T)
            targets (LongTensor): Batch of groundtruth durations in linear domain (B, T)

        Returns:
            Tensor: Mean squared error loss value.

        Note:
            `outputs` is in log domain but `targets` is in linear domain.

        """
        targets = torch.log(targets.float() + self.offset)
        loss = self.criterion(outputs, targets)
        return loss

<mask token>
import torch


class DurationPredictorLoss(torch.nn.Module):
    """Loss function module for duration predictor.

    The loss value is Calculated in log domain to make it Gaussian.

    """

    def __init__(self, offset=1.0, reduction='mean'):
        """Initilize duration predictor loss module.

        Args:
            offset (float, optional): Offset value to avoid nan in log domain.
            reduction (str): Reduction type in loss calculation.

        """
        super(DurationPredictorLoss, self).__init__()
        self.criterion = torch.nn.MSELoss(reduction=reduction)
        self.offset = offset

    def forward(self, outputs, targets):
        """Calculate forward propagation.

        Args:
            outputs (Tensor): Batch of prediction durations in log domain (B, T)
            targets (LongTensor): Batch of groundtruth durations in linear domain (B, T)

        Returns:
            Tensor: Mean squared error loss value.

        Note:
            `outputs` is in log domain but `targets` is in linear domain.

        """
        targets = torch.log(targets.float() + self.offset)
        loss = self.criterion(outputs, targets)
        return loss

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

# Copyright 2019 Tomoki Hayashi
#           2023 Jiatong SHi

# Adapted from espnet/espnet/net/pytorch_backend/duration_predictor.py
# Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)

"""Duration predictor related modules."""

import torch


class DurationPredictorLoss(torch.nn.Module):
    """Loss function module for duration predictor.

    The loss value is Calculated in log domain to make it Gaussian.

    """

    def __init__(self, offset=1.0, reduction="mean"):
        """Initilize duration predictor loss module.

        Args:
            offset (float, optional): Offset value to avoid nan in log domain.
            reduction (str): Reduction type in loss calculation.

        """
        super(DurationPredictorLoss, self).__init__()
        self.criterion = torch.nn.MSELoss(reduction=reduction)
        self.offset = offset

    def forward(self, outputs, targets):
        """Calculate forward propagation.

        Args:
            outputs (Tensor): Batch of prediction durations in log domain (B, T)
            targets (LongTensor): Batch of groundtruth durations in linear domain (B, T)

        Returns:
            Tensor: Mean squared error loss value.

        Note:
            `outputs` is in log domain but `targets` is in linear domain.

        """
        # NOTE: outputs is in log domain while targets in linear
        targets = torch.log(targets.float() + self.offset)
        loss = self.criterion(outputs, targets)

        return loss
