hifigan_ar_v2.py

"""
Neural network modules for the HiFi-GAN: Generative Adversarial Networks for
Efficient and High Fidelity Speech Synthesis

For more details: https://arxiv.org/pdf/2010.05646.pdf, https://arxiv.org/abs/2406.10735

Authors
 * Jarod Duret 2021
 * Yingzhi WANG 2022
"""

# Adapted from https://github.com/jik876/hifi-gan/ and https://github.com/coqui-ai/TTS/
# MIT License

# Copyright (c) 2020 Jungil Kong

# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.

# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.


import json
import logging
import math
import os
from typing import Tuple

import torch
import torch.nn as nn
import torch.nn.functional as F


LRELU_SLOPE = 0.1


def get_padding_elem(L_in: int, stride: int, kernel_size: int, dilation: int):
    """This function computes the number of elements to add for zero-padding.

    Arguments
    ---------
    L_in : int
    stride: int
    kernel_size : int
    dilation : int

    Returns
    -------
    padding : int
        The size of the padding to be added
    """
    if stride > 1:
        padding = [math.floor(kernel_size / 2), math.floor(kernel_size / 2)]

    else:
        L_out = (
            math.floor((L_in - dilation * (kernel_size - 1) - 1) / stride) + 1
        )
        padding = [
            math.floor((L_in - L_out) / 2),
            math.floor((L_in - L_out) / 2),
        ]
    return padding


def get_padding_elem_transposed(
    L_out: int,
    L_in: int,
    stride: int,
    kernel_size: int,
    dilation: int,
    output_padding: int,
):
    """This function computes the required padding size for transposed convolution

    Arguments
    ---------
    L_out : int
    L_in : int
    stride: int
    kernel_size : int
    dilation : int
    output_padding : int

    Returns
    -------
    padding : int
        The size of the padding to be applied
    """

    padding = -0.5 * (
        L_out
        - (L_in - 1) * stride
        - dilation * (kernel_size - 1)
        - output_padding
        - 1
    )
    return int(padding)


class Conv1d(nn.Module):
    """This function implements 1d convolution.

    Arguments
    ---------
    out_channels : int
        It is the number of output channels.
    kernel_size : int
        Kernel size of the convolutional filters.
    input_shape : tuple
        The shape of the input. Alternatively use ``in_channels``.
    in_channels : int
        The number of input channels. Alternatively use ``input_shape``.
    stride : int
        Stride factor of the convolutional filters. When the stride factor > 1,
        a decimation in time is performed.
    dilation : int
        Dilation factor of the convolutional filters.
    padding : str
        (same, valid, causal). If "valid", no padding is performed.
        If "same" and stride is 1, output shape is the same as the input shape.
        "causal" results in causal (dilated) convolutions.
    groups : int
        Number of blocked connections from input channels to output channels.
    bias : bool
        Whether to add a bias term to convolution operation.
    padding_mode : str
        This flag specifies the type of padding. See torch.nn documentation
        for more information.
    skip_transpose : bool
        If False, uses batch x time x channel convention of speechbrain.
        If True, uses batch x channel x time convention.
    weight_norm : bool
        If True, use weight normalization,
        to be removed with self.remove_weight_norm() at inference
    conv_init : str
        Weight initialization for the convolution network
    default_padding: str or int
        This sets the default padding mode that will be used by the pytorch Conv1d backend.

    Example
    -------
    >>> inp_tensor = torch.rand([10, 40, 16])
    >>> cnn_1d = Conv1d(
    ...     input_shape=inp_tensor.shape, out_channels=8, kernel_size=5
    ... )
    >>> out_tensor = cnn_1d(inp_tensor)
    >>> out_tensor.shape
    torch.Size([10, 40, 8])
    """

    def __init__(
        self,
        out_channels,
        kernel_size,
        input_shape=None,
        in_channels=None,
        stride=1,
        dilation=1,
        padding="same",
        groups=1,
        bias=True,
        padding_mode="reflect",
        skip_transpose=False,
        weight_norm=False,
        conv_init=None,
        default_padding=0,
    ):
        super().__init__()
        self.kernel_size = kernel_size
        self.stride = stride
        self.dilation = dilation
        self.padding = padding
        self.padding_mode = padding_mode
        self.unsqueeze = False
        self.skip_transpose = skip_transpose

        if input_shape is None and in_channels is None:
            raise ValueError("Must provide one of input_shape or in_channels")

        if in_channels is None:
            in_channels = self._check_input_shape(input_shape)

        self.in_channels = in_channels

        self.conv = nn.Conv1d(
            in_channels,
            out_channels,
            self.kernel_size,
            stride=self.stride,
            dilation=self.dilation,
            padding=default_padding,
            groups=groups,
            bias=bias,
        )

        if conv_init == "kaiming":
            nn.init.kaiming_normal_(self.conv.weight)
        elif conv_init == "zero":
            nn.init.zeros_(self.conv.weight)
        elif conv_init == "normal":
            nn.init.normal_(self.conv.weight, std=1e-6)

        if weight_norm:
            self.conv = nn.utils.weight_norm(self.conv)

    def forward(self, x):
        """Returns the output of the convolution.

        Arguments
        ---------
        x : torch.Tensor (batch, time, channel)
            input to convolve. 2d or 4d tensors are expected.

        Returns
        -------
        wx : torch.Tensor
            The convolved outputs.
        """
        if not self.skip_transpose:
            x = x.transpose(1, -1)

        if self.unsqueeze:
            x = x.unsqueeze(1)

        if self.padding == "same":
            x = self._manage_padding(
                x, self.kernel_size, self.dilation, self.stride
            )

        elif self.padding == "causal":
            num_pad = (self.kernel_size - 1) * self.dilation
            x = F.pad(x, (num_pad, 0))

        elif self.padding == "valid":
            pass

        else:
            raise ValueError(
                "Padding must be 'same', 'valid' or 'causal'. Got "
                + self.padding
            )

        wx = self.conv(x)

        if self.unsqueeze:
            wx = wx.squeeze(1)

        if not self.skip_transpose:
            wx = wx.transpose(1, -1)

        return wx

    def _manage_padding(self, x, kernel_size: int, dilation: int, stride: int):
        """This function performs zero-padding on the time axis
        such that their lengths is unchanged after the convolution.

        Arguments
        ---------
        x : torch.Tensor
            Input tensor.
        kernel_size : int
            Size of kernel.
        dilation : int
            Dilation used.
        stride : int
            Stride.

        Returns
        -------
        x : torch.Tensor
            The padded outputs.
        """

        # Detecting input shape
        L_in = self.in_channels

        # Time padding
        padding = get_padding_elem(L_in, stride, kernel_size, dilation)

        # Applying padding
        x = F.pad(x, padding, mode=self.padding_mode)

        return x

    def _check_input_shape(self, shape):
        """Checks the input shape and returns the number of input channels."""

        if len(shape) == 2:
            self.unsqueeze = True
            in_channels = 1
        elif self.skip_transpose:
            in_channels = shape[1]
        elif len(shape) == 3:
            in_channels = shape[2]
        else:
            raise ValueError(
                "conv1d expects 2d, 3d inputs. Got " + str(len(shape))
            )

        # Kernel size must be odd
        if not self.padding == "valid" and self.kernel_size % 2 == 0:
            raise ValueError(
                "The field kernel size must be an odd number. Got %s."
                % (self.kernel_size)
            )

        return in_channels

    def remove_weight_norm(self):
        """Removes weight normalization at inference if used during training."""
        self.conv = nn.utils.remove_weight_norm(self.conv)


class Conv2d(nn.Module):
    """This function implements 2d convolution.

    Arguments
    ---------
    out_channels : int
        It is the number of output channels.
    kernel_size : tuple
        Kernel size of the 2d convolutional filters over time and frequency
        axis.
    input_shape : tuple
        The shape of the input. Alternatively use ``in_channels``.
    in_channels : int
        The number of input channels. Alternatively use ``input_shape``.
    stride: int
        Stride factor of the 2d convolutional filters over time and frequency
        axis.
    dilation : int
        Dilation factor of the 2d convolutional filters over time and
        frequency axis.
    padding : str
        (same, valid, causal).
        If "valid", no padding is performed.
        If "same" and stride is 1, output shape is same as input shape.
        If "causal" then proper padding is inserted to simulate causal convolution on the first spatial dimension.
        (spatial dim 1 is dim 3 for both skip_transpose=False and skip_transpose=True)
    groups : int
        This option specifies the convolutional groups. See torch.nn
        documentation for more information.
    bias : bool
        If True, the additive bias b is adopted.
    padding_mode : str
        This flag specifies the type of padding. See torch.nn documentation
        for more information.
    max_norm : float
        kernel max-norm.
    swap : bool
        If True, the convolution is done with the format (B, C, W, H).
        If False, the convolution is dine with (B, H, W, C).
        Active only if skip_transpose is False.
    skip_transpose : bool
        If False, uses batch x spatial.dim2 x spatial.dim1 x channel convention of speechbrain.
        If True, uses batch x channel x spatial.dim1 x spatial.dim2 convention.
    weight_norm : bool
        If True, use weight normalization,
        to be removed with self.remove_weight_norm() at inference
    conv_init : str
        Weight initialization for the convolution network

    Example
    -------
    >>> inp_tensor = torch.rand([10, 40, 16, 8])
    >>> cnn_2d = Conv2d(
    ...     input_shape=inp_tensor.shape, out_channels=5, kernel_size=(7, 3)
    ... )
    >>> out_tensor = cnn_2d(inp_tensor)
    >>> out_tensor.shape
    torch.Size([10, 40, 16, 5])
    """

    def __init__(
        self,
        out_channels,
        kernel_size,
        input_shape=None,
        in_channels=None,
        stride=(1, 1),
        dilation=(1, 1),
        padding="same",
        groups=1,
        bias=True,
        padding_mode="reflect",
        max_norm=None,
        swap=False,
        skip_transpose=False,
        weight_norm=False,
        conv_init=None,
    ):
        super().__init__()

        # handle the case if some parameter is int
        if isinstance(kernel_size, int):
            kernel_size = (kernel_size, kernel_size)
        if isinstance(stride, int):
            stride = (stride, stride)
        if isinstance(dilation, int):
            dilation = (dilation, dilation)

        self.kernel_size = kernel_size
        self.stride = stride
        self.dilation = dilation
        self.padding = padding
        self.padding_mode = padding_mode
        self.unsqueeze = False
        self.max_norm = max_norm
        self.swap = swap
        self.skip_transpose = skip_transpose

        if input_shape is None and in_channels is None:
            raise ValueError("Must provide one of input_shape or in_channels")

        if in_channels is None:
            in_channels = self._check_input(input_shape)

        self.in_channels = in_channels

        # Weights are initialized following pytorch approach
        self.conv = nn.Conv2d(
            self.in_channels,
            out_channels,
            self.kernel_size,
            stride=self.stride,
            padding=0,
            dilation=self.dilation,
            groups=groups,
            bias=bias,
        )

        if conv_init == "kaiming":
            nn.init.kaiming_normal_(self.conv.weight)
        elif conv_init == "zero":
            nn.init.zeros_(self.conv.weight)

        if weight_norm:
            self.conv = nn.utils.weight_norm(self.conv)

    def forward(self, x):
        """Returns the output of the convolution.

        Arguments
        ---------
        x : torch.Tensor (batch, time, channel)
            input to convolve. 2d or 4d tensors are expected.

        Returns
        -------
        x : torch.Tensor
            The output of the convolution.
        """
        if not self.skip_transpose:
            x = x.transpose(1, -1)
            if self.swap:
                x = x.transpose(-1, -2)

        if self.unsqueeze:
            x = x.unsqueeze(1)

        if self.padding == "same":
            x = self._manage_padding(
                x, self.kernel_size, self.dilation, self.stride
            )

        elif self.padding == "causal":
            num_pad = (self.kernel_size[0] - 1) * self.dilation[1]
            x = F.pad(x, (0, 0, num_pad, 0))

        elif self.padding == "valid":
            pass

        else:
            raise ValueError(
                "Padding must be 'same','valid' or 'causal'. Got "
                + self.padding
            )

        if self.max_norm is not None:
            self.conv.weight.data = torch.renorm(
                self.conv.weight.data, p=2, dim=0, maxnorm=self.max_norm
            )

        wx = self.conv(x)

        if self.unsqueeze:
            wx = wx.squeeze(1)

        if not self.skip_transpose:
            wx = wx.transpose(1, -1)
            if self.swap:
                wx = wx.transpose(1, 2)
        return wx

    def _manage_padding(
        self,
        x,
        kernel_size: Tuple[int, int],
        dilation: Tuple[int, int],
        stride: Tuple[int, int],
    ):
        """This function performs zero-padding on the time and frequency axes
        such that their lengths is unchanged after the convolution.

        Arguments
        ---------
        x : torch.Tensor
            Input to be padded
        kernel_size : int
            Size of the kernel for computing padding
        dilation : int
            Dilation rate for computing padding
        stride: int
            Stride for computing padding

        Returns
        -------
        x : torch.Tensor
            The padded outputs.
        """
        # Detecting input shape
        L_in = self.in_channels

        # Time padding
        padding_time = get_padding_elem(
            L_in, stride[-1], kernel_size[-1], dilation[-1]
        )

        padding_freq = get_padding_elem(
            L_in, stride[-2], kernel_size[-2], dilation[-2]
        )
        padding = padding_time + padding_freq

        # Applying padding
        x = nn.functional.pad(x, padding, mode=self.padding_mode)

        return x

    def _check_input(self, shape):
        """Checks the input shape and returns the number of input channels."""

        if len(shape) == 3:
            self.unsqueeze = True
            in_channels = 1

        elif len(shape) == 4:
            in_channels = shape[3]

        else:
            raise ValueError("Expected 3d or 4d inputs. Got " + len(shape))

        # Kernel size must be odd
        if not self.padding == "valid" and (
            self.kernel_size[0] % 2 == 0 or self.kernel_size[1] % 2 == 0
        ):
            raise ValueError(
                "The field kernel size must be an odd number. Got %s."
                % (self.kernel_size)
            )

        return in_channels

    def remove_weight_norm(self):
        """Removes weight normalization at inference if used during training."""
        self.conv = nn.utils.remove_weight_norm(self.conv)


class ConvTranspose1d(nn.Module):
    """This class implements 1d transposed convolution with speechbrain.
    Transpose convolution is normally used to perform upsampling.

    Arguments
    ---------
    out_channels : int
        It is the number of output channels.
    kernel_size : int
        Kernel size of the convolutional filters.
    input_shape : tuple
        The shape of the input. Alternatively use ``in_channels``.
    in_channels : int
        The number of input channels. Alternatively use ``input_shape``.
    stride : int
        Stride factor of the convolutional filters. When the stride factor > 1,
        upsampling in time is performed.
    dilation : int
        Dilation factor of the convolutional filters.
    padding : str or int
        To have in output the target dimension, we suggest tuning the kernel
        size and the padding properly. We also support the following function
        to have some control over the padding and the corresponding output
        dimensionality.
        if "valid", no padding is applied
        if "same", padding amount is inferred so that the output size is closest
        to possible to input size. Note that for some kernel_size / stride combinations
        it is not possible to obtain the exact same size, but we return the closest
        possible size.
        if "factor", padding amount is inferred so that the output size is closest
        to inputsize*stride. Note that for some kernel_size / stride combinations
        it is not possible to obtain the exact size, but we return the closest
        possible size.
        if an integer value is entered, a custom padding is used.
    output_padding : int,
        Additional size added to one side of the output shape
    groups: int
        Number of blocked connections from input channels to output channels.
        Default: 1
    bias: bool
        If True, adds a learnable bias to the output
    skip_transpose : bool
        If False, uses batch x time x channel convention of speechbrain.
        If True, uses batch x channel x time convention.
    weight_norm : bool
        If True, use weight normalization,
        to be removed with self.remove_weight_norm() at inference

    Example
    -------
    >>> from speechbrain.nnet.CNN import Conv1d, ConvTranspose1d
    >>> inp_tensor = torch.rand([10, 12, 40]) #[batch, time, fea]
    >>> convtranspose_1d = ConvTranspose1d(
    ...     input_shape=inp_tensor.shape, out_channels=8, kernel_size=3, stride=2
    ... )
    >>> out_tensor = convtranspose_1d(inp_tensor)
    >>> out_tensor.shape
    torch.Size([10, 25, 8])

    >>> # Combination of Conv1d and ConvTranspose1d
    >>> from speechbrain.nnet.CNN import Conv1d, ConvTranspose1d
    >>> signal = torch.tensor([1,100])
    >>> signal = torch.rand([1,100]) #[batch, time]
    >>> conv1d = Conv1d(input_shape=signal.shape, out_channels=1, kernel_size=3, stride=2)
    >>> conv_out = conv1d(signal)
    >>> conv_t = ConvTranspose1d(input_shape=conv_out.shape, out_channels=1, kernel_size=3, stride=2, padding=1)
    >>> signal_rec = conv_t(conv_out, output_size=[100])
    >>> signal_rec.shape
    torch.Size([1, 100])

    >>> signal = torch.rand([1,115]) #[batch, time]
    >>> conv_t = ConvTranspose1d(input_shape=signal.shape, out_channels=1, kernel_size=3, stride=2, padding='same')
    >>> signal_rec = conv_t(signal)
    >>> signal_rec.shape
    torch.Size([1, 115])

    >>> signal = torch.rand([1,115]) #[batch, time]
    >>> conv_t = ConvTranspose1d(input_shape=signal.shape, out_channels=1, kernel_size=7, stride=2, padding='valid')
    >>> signal_rec = conv_t(signal)
    >>> signal_rec.shape
    torch.Size([1, 235])

    >>> signal = torch.rand([1,115]) #[batch, time]
    >>> conv_t = ConvTranspose1d(input_shape=signal.shape, out_channels=1, kernel_size=7, stride=2, padding='factor')
    >>> signal_rec = conv_t(signal)
    >>> signal_rec.shape
    torch.Size([1, 231])

    >>> signal = torch.rand([1,115]) #[batch, time]
    >>> conv_t = ConvTranspose1d(input_shape=signal.shape, out_channels=1, kernel_size=3, stride=2, padding=10)
    >>> signal_rec = conv_t(signal)
    >>> signal_rec.shape
    torch.Size([1, 211])

    """

    def __init__(
        self,
        out_channels,
        kernel_size,
        input_shape=None,
        in_channels=None,
        stride=1,
        dilation=1,
        padding=0,
        output_padding=0,
        groups=1,
        bias=True,
        skip_transpose=False,
        weight_norm=False,
    ):
        super().__init__()
        self.kernel_size = kernel_size
        self.stride = stride
        self.dilation = dilation
        self.padding = padding
        self.unsqueeze = False
        self.skip_transpose = skip_transpose

        if input_shape is None and in_channels is None:
            raise ValueError("Must provide one of input_shape or in_channels")

        if in_channels is None:
            in_channels = self._check_input_shape(input_shape)

        if self.padding == "same":
            L_in = input_shape[-1] if skip_transpose else input_shape[1]
            padding_value = get_padding_elem_transposed(
                L_in,
                L_in,
                stride=stride,
                kernel_size=kernel_size,
                dilation=dilation,
                output_padding=output_padding,
            )
        elif self.padding == "factor":
            L_in = input_shape[-1] if skip_transpose else input_shape[1]
            padding_value = get_padding_elem_transposed(
                L_in * stride,
                L_in,
                stride=stride,
                kernel_size=kernel_size,
                dilation=dilation,
                output_padding=output_padding,
            )
        elif self.padding == "valid":
            padding_value = 0
        elif type(self.padding) is int:
            padding_value = padding
        else:
            raise ValueError("Not supported padding type")

        self.conv = nn.ConvTranspose1d(
            in_channels,
            out_channels,
            self.kernel_size,
            stride=self.stride,
            dilation=self.dilation,
            padding=padding_value,
            groups=groups,
            bias=bias,
        )

        if weight_norm:
            self.conv = nn.utils.weight_norm(self.conv)

    def forward(self, x, output_size=None):
        """Returns the output of the convolution.

        Arguments
        ---------
        x : torch.Tensor (batch, time, channel)
            input to convolve. 2d or 4d tensors are expected.
        output_size : int
            The size of the output

        Returns
        -------
        x : torch.Tensor
            The convolved output
        """

        if not self.skip_transpose:
            x = x.transpose(1, -1)

        if self.unsqueeze:
            x = x.unsqueeze(1)

        wx = self.conv(x, output_size=output_size)

        if self.unsqueeze:
            wx = wx.squeeze(1)

        if not self.skip_transpose:
            wx = wx.transpose(1, -1)

        return wx

    def _check_input_shape(self, shape):
        """Checks the input shape and returns the number of input channels."""

        if len(shape) == 2:
            self.unsqueeze = True
            in_channels = 1
        elif self.skip_transpose:
            in_channels = shape[1]
        elif len(shape) == 3:
            in_channels = shape[2]
        else:
            raise ValueError(
                "conv1d expects 2d, 3d inputs. Got " + str(len(shape))
            )

        return in_channels

    def remove_weight_norm(self):
        """Removes weight normalization at inference if used during training."""
        self.conv = nn.utils.remove_weight_norm(self.conv)


class ResBlock1(torch.nn.Module):
    """
    Residual Block Type 1, which has 3 convolutional layers in each convolution block.

    Arguments
    ---------
    channels : int
        number of hidden channels for the convolutional layers.
    kernel_size : int
        size of the convolution filter in each layer.
    dilation : list
        list of dilation value for each conv layer in a block.
    """

    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
        super().__init__()
        self.convs1 = nn.ModuleList(
            [
                Conv1d(
                    in_channels=channels,
                    out_channels=channels,
                    kernel_size=kernel_size,
                    stride=1,
                    dilation=dilation[0],
                    padding="same",
                    skip_transpose=True,
                    weight_norm=True,
                ),
                Conv1d(
                    in_channels=channels,
                    out_channels=channels,
                    kernel_size=kernel_size,
                    stride=1,
                    dilation=dilation[1],
                    padding="same",
                    skip_transpose=True,
                    weight_norm=True,
                ),
                Conv1d(
                    in_channels=channels,
                    out_channels=channels,
                    kernel_size=kernel_size,
                    stride=1,
                    dilation=dilation[2],
                    padding="same",
                    skip_transpose=True,
                    weight_norm=True,
                ),
            ]
        )

        self.convs2 = nn.ModuleList(
            [
                Conv1d(
                    in_channels=channels,
                    out_channels=channels,
                    kernel_size=kernel_size,
                    stride=1,
                    dilation=1,
                    padding="same",
                    skip_transpose=True,
                    weight_norm=True,
                ),
                Conv1d(
                    in_channels=channels,
                    out_channels=channels,
                    kernel_size=kernel_size,
                    stride=1,
                    dilation=1,
                    padding="same",
                    skip_transpose=True,
                    weight_norm=True,
                ),
                Conv1d(
                    in_channels=channels,
                    out_channels=channels,
                    kernel_size=kernel_size,
                    stride=1,
                    dilation=1,
                    padding="same",
                    skip_transpose=True,
                    weight_norm=True,
                ),
            ]
        )

    def forward(self, x):
        """Returns the output of ResBlock1

        Arguments
        ---------
        x : torch.Tensor (batch, channel, time)
            input tensor.

        Returns
        -------
        The ResBlock outputs
        """

        for c1, c2 in zip(self.convs1, self.convs2):
            xt = F.leaky_relu(x, LRELU_SLOPE)
            xt = c1(xt)
            xt = F.leaky_relu(xt, LRELU_SLOPE)
            xt = c2(xt)
            x = xt + x
        return x

    def remove_weight_norm(self):
        """This functions removes weight normalization during inference."""
        for layer in self.convs1:
            layer.remove_weight_norm()
        for layer in self.convs2:
            layer.remove_weight_norm()


class ResBlock2(torch.nn.Module):
    """
    Residual Block Type 2, which has 2 convolutional layers in each convolution block.

    Arguments
    ---------
    channels : int
        number of hidden channels for the convolutional layers.
    kernel_size : int
        size of the convolution filter in each layer.
    dilation : list
        list of dilation value for each conv layer in a block.
    """

    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
        super().__init__()
        self.convs = nn.ModuleList(
            [
                Conv1d(
                    in_channels=channels,
                    out_channels=channels,
                    kernel_size=kernel_size,
                    stride=1,
                    dilation=dilation[0],
                    padding="same",
                    skip_transpose=True,
                    weight_norm=True,
                ),
                Conv1d(
                    in_channels=channels,
                    out_channels=channels,
                    kernel_size=kernel_size,
                    stride=1,
                    dilation=dilation[1],
                    padding="same",
                    skip_transpose=True,
                    weight_norm=True,
                ),
            ]
        )

    def forward(self, x):
        """Returns the output of ResBlock1

        Arguments
        ---------
        x : torch.Tensor (batch, channel, time)
            input tensor.

        Returns
        -------
        The ResBlock outputs
        """

        for c in self.convs:
            xt = F.leaky_relu(x, LRELU_SLOPE)
            xt = c(xt)
            x = xt + x
        return x

    def remove_weight_norm(self):
        """This functions removes weight normalization during inference."""
        for layer in self.convs:
            layer.remove_weight_norm()


class HiFiGANArabicGenerator(torch.nn.Module):
    """HiFiGAN Generator with Multi-Receptive Field Fusion (MRF)

    Arguments
    ---------
    in_channels : int
        number of input tensor channels.
    out_channels : int
        number of output tensor channels.
    resblock_type : str
        type of the `ResBlock`. '1' or '2'.
    resblock_dilation_sizes : List[List[int]]
        list of dilation values in each layer of a `ResBlock`.
    resblock_kernel_sizes : List[int]
        list of kernel sizes for each `ResBlock`.
    upsample_kernel_sizes : List[int]
        list of kernel sizes for each transposed convolution.
    upsample_initial_channel : int
        number of channels for the first upsampling layer. This is divided by 2
        for each consecutive upsampling layer.
    upsample_factors : List[int]
        upsampling factors (stride) for each upsampling layer.
    inference_padding : int
       constant padding applied to the input at inference time. Defaults to 5.
    cond_channels : int
        If provided, adds a conv layer to the beginning of the forward.
    conv_post_bias : bool
        Whether to add a bias term to the final conv.

    Example
    -------
    >>> inp_tensor = torch.rand([4, 80, 33])
    >>> hifigan_generator= HifiganGenerator(
    ...    in_channels = 80,
    ...    out_channels = 1,
    ...    resblock_type = "1",
    ...    resblock_dilation_sizes = [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
    ...    resblock_kernel_sizes = [3, 7, 11],
    ...    upsample_kernel_sizes = [16, 16, 4, 4],
    ...    upsample_initial_channel = 512,
    ...    upsample_factors = [8, 8, 2, 2],
    ... )
    >>> out_tensor = hifigan_generator(inp_tensor)
    >>> out_tensor.shape
    torch.Size([4, 1, 8448])
    """

    def __init__(
        self,
        in_channels,
        out_channels,
        resblock_type,
        resblock_dilation_sizes,
        resblock_kernel_sizes,
        upsample_kernel_sizes,
        upsample_initial_channel,
        upsample_factors,
        inference_padding=5,
        cond_channels=0,
        conv_post_bias=True,
    ):
        super().__init__()
        self.inference_padding = inference_padding
        self.num_kernels = len(resblock_kernel_sizes)
        self.num_upsamples = len(upsample_factors)
        # initial upsampling layers
        self.conv_pre = Conv1d(
            in_channels=in_channels,
            out_channels=upsample_initial_channel,
            kernel_size=7,
            stride=1,
            padding="same",
            skip_transpose=True,
            weight_norm=True,
        )
        resblock = ResBlock1 if resblock_type == "1" else ResBlock2
        # upsampling layers
        self.ups = nn.ModuleList()
        for i, (u, k) in enumerate(
            zip(upsample_factors, upsample_kernel_sizes)
        ):
            self.ups.append(
                ConvTranspose1d(
                    in_channels=upsample_initial_channel // (2**i),
                    out_channels=upsample_initial_channel // (2 ** (i + 1)),
                    kernel_size=k,
                    stride=u,
                    padding=(k - u) // 2,
                    skip_transpose=True,
                    weight_norm=True,
                )
            )
        # MRF blocks
        self.resblocks = nn.ModuleList()
        for i in range(len(self.ups)):
            ch = upsample_initial_channel // (2 ** (i + 1))
            for _, (k, d) in enumerate(
                zip(resblock_kernel_sizes, resblock_dilation_sizes)
            ):
                self.resblocks.append(resblock(ch, k, d))
        # post convolution layer
        self.conv_post = Conv1d(
            in_channels=ch,
            out_channels=1,
            kernel_size=7,
            stride=1,
            padding="same",
            skip_transpose=True,
            bias=conv_post_bias,
            weight_norm=True,
        )
        if cond_channels > 0:
            self.cond_layer = Conv1d(
                in_channels=cond_channels,
                out_channels=upsample_initial_channel,
                kernel_size=1,
            )

    def forward(self, x, g=None):
        """
        Arguments
        ---------
        x : torch.Tensor (batch, channel, time)
            feature input tensor.
        g : torch.Tensor (batch, 1, time)
            global conditioning input tensor.

        Returns
        -------
        The generator outputs
        """

        o = self.conv_pre(x)
        if hasattr(self, "cond_layer"):
            o = o + self.cond_layer(g)
        for i in range(self.num_upsamples):
            o = F.leaky_relu(o, LRELU_SLOPE)
            o = self.ups[i](o)
            z_sum = None
            for j in range(self.num_kernels):
                if z_sum is None:
                    z_sum = self.resblocks[i * self.num_kernels + j](o)
                else:
                    z_sum += self.resblocks[i * self.num_kernels + j](o)
            o = z_sum / self.num_kernels
        o = F.leaky_relu(o)
        o = self.conv_post(o)
        o = torch.tanh(o)
        return o

    def remove_weight_norm(self):
        """This functions removes weight normalization during inference."""

        for layer in self.ups:
            layer.remove_weight_norm()
        for layer in self.resblocks:
            layer.remove_weight_norm()
        self.conv_pre.remove_weight_norm()
        self.conv_post.remove_weight_norm()

    @torch.no_grad()
    def inference(self, c, padding=True):
        """The inference function performs a padding and runs the forward method.

        Arguments
        ---------
        c : torch.Tensor (batch, channel, time)
            feature input tensor.
        padding : bool
            Whether to pad tensor before forward.

        Returns
        -------
        The generator outputs
        """
        if padding:
            c = torch.nn.functional.pad(
                c, (self.inference_padding, self.inference_padding), "replicate"
            )
        return self.forward(c)

    @classmethod
    def from_pretrained(cls, checkpoint_path, config_path=None, device='cpu'):
        if config_path is None:
            config_path = os.path.join(os.path.dirname(__file__), "config.json")
        with open(config_path, "r") as file:
            config = json.load(file)
        model = cls(**config)
        ckpt = torch.load(checkpoint_path, map_location='cpu')
        model.load_state_dict(ckpt)
        return model.eval().to(device)



if __name__ == '__main__':
    gen = HifiganGenerator.from_pretrained("generator.ckpt", "config.json")
    x = torch.rand(1, 80, 122)
    mel = gen(x)