models.py

# coding:utf-8

import os
import os.path as osp

import copy
import math

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm

from Utils.ASR.models import ASRCNN
from Utils.JDC.model import JDCNet

from Modules.diffusion.sampler import KDiffusion, LogNormalDistribution
from Modules.diffusion.modules import Transformer1d, StyleTransformer1d
from Modules.diffusion.diffusion import AudioDiffusionConditional

from munch import Munch
import yaml


class LearnedDownSample(nn.Module):
    def __init__(self, layer_type, dim_in):
        super().__init__()
        self.layer_type = layer_type

        if self.layer_type == 'none':
            self.conv = nn.Identity()
        elif self.layer_type == 'timepreserve':
            self.conv = spectral_norm(
                nn.Conv2d(dim_in, dim_in, kernel_size=(3, 1), stride=(2, 1), groups=dim_in, padding=(1, 0)))
        elif self.layer_type == 'half':
            self.conv = spectral_norm(
                nn.Conv2d(dim_in, dim_in, kernel_size=(3, 3), stride=(2, 2), groups=dim_in, padding=1))
        else:
            raise RuntimeError(
                'Got unexpected donwsampletype %s, expected is [none, timepreserve, half]' % self.layer_type)

    def forward(self, x):
        return self.conv(x)


class LearnedUpSample(nn.Module):
    def __init__(self, layer_type, dim_in):
        super().__init__()
        self.layer_type = layer_type

        if self.layer_type == 'none':
            self.conv = nn.Identity()
        elif self.layer_type == 'timepreserve':
            self.conv = nn.ConvTranspose2d(dim_in, dim_in, kernel_size=(3, 1), stride=(2, 1), groups=dim_in,
                                           output_padding=(1, 0), padding=(1, 0))
        elif self.layer_type == 'half':
            self.conv = nn.ConvTranspose2d(dim_in, dim_in, kernel_size=(3, 3), stride=(2, 2), groups=dim_in,
                                           output_padding=1, padding=1)
        else:
            raise RuntimeError(
                'Got unexpected upsampletype %s, expected is [none, timepreserve, half]' % self.layer_type)

    def forward(self, x):
        return self.conv(x)


class DownSample(nn.Module):
    def __init__(self, layer_type):
        super().__init__()
        self.layer_type = layer_type

    def forward(self, x):
        if self.layer_type == 'none':
            return x
        elif self.layer_type == 'timepreserve':
            return F.avg_pool2d(x, (2, 1))
        elif self.layer_type == 'half':
            if x.shape[-1] % 2 != 0:
                x = torch.cat([x, x[..., -1].unsqueeze(-1)], dim=-1)
            return F.avg_pool2d(x, 2)
        else:
            raise RuntimeError(
                'Got unexpected donwsampletype %s, expected is [none, timepreserve, half]' % self.layer_type)


class UpSample(nn.Module):
    def __init__(self, layer_type):
        super().__init__()
        self.layer_type = layer_type

    def forward(self, x):
        if self.layer_type == 'none':
            return x
        elif self.layer_type == 'timepreserve':
            return F.interpolate(x, scale_factor=(2, 1), mode='nearest')
        elif self.layer_type == 'half':
            return F.interpolate(x, scale_factor=2, mode='nearest')
        else:
            raise RuntimeError(
                'Got unexpected upsampletype %s, expected is [none, timepreserve, half]' % self.layer_type)


class ResBlk(nn.Module):
    def __init__(self, dim_in, dim_out, actv=nn.LeakyReLU(0.2),
                 normalize=False, downsample='none'):
        super().__init__()
        self.actv = actv
        self.normalize = normalize
        self.downsample = DownSample(downsample)
        self.downsample_res = LearnedDownSample(downsample, dim_in)
        self.learned_sc = dim_in != dim_out
        self._build_weights(dim_in, dim_out)

    def _build_weights(self, dim_in, dim_out):
        self.conv1 = spectral_norm(nn.Conv2d(dim_in, dim_in, 3, 1, 1))
        self.conv2 = spectral_norm(nn.Conv2d(dim_in, dim_out, 3, 1, 1))
        if self.normalize:
            self.norm1 = nn.InstanceNorm2d(dim_in, affine=True)
            self.norm2 = nn.InstanceNorm2d(dim_in, affine=True)
        if self.learned_sc:
            self.conv1x1 = spectral_norm(nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False))

    def _shortcut(self, x):
        if self.learned_sc:
            x = self.conv1x1(x)
        if self.downsample:
            x = self.downsample(x)
        return x

    def _residual(self, x):
        if self.normalize:
            x = self.norm1(x)
        x = self.actv(x)
        x = self.conv1(x)
        x = self.downsample_res(x)
        if self.normalize:
            x = self.norm2(x)
        x = self.actv(x)
        x = self.conv2(x)
        return x

    def forward(self, x):
        x = self._shortcut(x) + self._residual(x)
        return x / math.sqrt(2)  # unit variance


class StyleEncoder(nn.Module):
    def __init__(self, dim_in=48, style_dim=48, max_conv_dim=384):
        super().__init__()
        blocks = []
        blocks += [spectral_norm(nn.Conv2d(1, dim_in, 3, 1, 1))]

        repeat_num = 4
        for _ in range(repeat_num):
            dim_out = min(dim_in * 2, max_conv_dim)
            blocks += [ResBlk(dim_in, dim_out, downsample='half')]
            dim_in = dim_out

        blocks += [nn.LeakyReLU(0.2)]
        blocks += [spectral_norm(nn.Conv2d(dim_out, dim_out, 5, 1, 0))]
        blocks += [nn.AdaptiveAvgPool2d(1)]
        blocks += [nn.LeakyReLU(0.2)]
        self.shared = nn.Sequential(*blocks)

        self.unshared = nn.Linear(dim_out, style_dim)

    def forward(self, x):
        h = self.shared(x)
        h = h.view(h.size(0), -1)
        s = self.unshared(h)

        return s


class LinearNorm(torch.nn.Module):
    def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
        super(LinearNorm, self).__init__()
        self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)

        torch.nn.init.xavier_uniform_(
            self.linear_layer.weight,
            gain=torch.nn.init.calculate_gain(w_init_gain))

    def forward(self, x):
        return self.linear_layer(x)


class Discriminator2d(nn.Module):
    def __init__(self, dim_in=48, num_domains=1, max_conv_dim=384, repeat_num=4):
        super().__init__()
        blocks = []
        blocks += [spectral_norm(nn.Conv2d(1, dim_in, 3, 1, 1))]

        for lid in range(repeat_num):
            dim_out = min(dim_in * 2, max_conv_dim)
            blocks += [ResBlk(dim_in, dim_out, downsample='half')]
            dim_in = dim_out

        blocks += [nn.LeakyReLU(0.2)]
        blocks += [spectral_norm(nn.Conv2d(dim_out, dim_out, 5, 1, 0))]
        blocks += [nn.LeakyReLU(0.2)]
        blocks += [nn.AdaptiveAvgPool2d(1)]
        blocks += [spectral_norm(nn.Conv2d(dim_out, num_domains, 1, 1, 0))]
        self.main = nn.Sequential(*blocks)

    def get_feature(self, x):
        features = []
        for l in self.main:
            x = l(x)
            features.append(x)
        out = features[-1]
        out = out.view(out.size(0), -1)  # (batch, num_domains)
        return out, features

    def forward(self, x):
        out, features = self.get_feature(x)
        out = out.squeeze()  # (batch)
        return out, features


class ResBlk1d(nn.Module):
    def __init__(self, dim_in, dim_out, actv=nn.LeakyReLU(0.2),
                 normalize=False, downsample='none', dropout_p=0.2):
        super().__init__()
        self.actv = actv
        self.normalize = normalize
        self.downsample_type = downsample
        self.learned_sc = dim_in != dim_out
        self._build_weights(dim_in, dim_out)
        self.dropout_p = dropout_p

        if self.downsample_type == 'none':
            self.pool = nn.Identity()
        else:
            self.pool = weight_norm(nn.Conv1d(dim_in, dim_in, kernel_size=3, stride=2, groups=dim_in, padding=1))

    def _build_weights(self, dim_in, dim_out):
        self.conv1 = weight_norm(nn.Conv1d(dim_in, dim_in, 3, 1, 1))
        self.conv2 = weight_norm(nn.Conv1d(dim_in, dim_out, 3, 1, 1))
        if self.normalize:
            self.norm1 = nn.InstanceNorm1d(dim_in, affine=True)
            self.norm2 = nn.InstanceNorm1d(dim_in, affine=True)
        if self.learned_sc:
            self.conv1x1 = weight_norm(nn.Conv1d(dim_in, dim_out, 1, 1, 0, bias=False))

    def downsample(self, x):
        if self.downsample_type == 'none':
            return x
        else:
            if x.shape[-1] % 2 != 0:
                x = torch.cat([x, x[..., -1].unsqueeze(-1)], dim=-1)
            return F.avg_pool1d(x, 2)

    def _shortcut(self, x):
        if self.learned_sc:
            x = self.conv1x1(x)
        x = self.downsample(x)
        return x

    def _residual(self, x):
        if self.normalize:
            x = self.norm1(x)
        x = self.actv(x)
        x = F.dropout(x, p=self.dropout_p, training=self.training)

        x = self.conv1(x)
        x = self.pool(x)
        if self.normalize:
            x = self.norm2(x)

        x = self.actv(x)
        x = F.dropout(x, p=self.dropout_p, training=self.training)

        x = self.conv2(x)
        return x

    def forward(self, x):
        x = self._shortcut(x) + self._residual(x)
        return x / math.sqrt(2)  # unit variance


class LayerNorm(nn.Module):
    def __init__(self, channels, eps=1e-5):
        super().__init__()
        self.channels = channels
        self.eps = eps

        self.gamma = nn.Parameter(torch.ones(channels))
        self.beta = nn.Parameter(torch.zeros(channels))

    def forward(self, x):
        x = x.transpose(1, -1)
        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
        return x.transpose(1, -1)


class TextEncoder(nn.Module):
    def __init__(self, channels, kernel_size, depth, n_symbols, actv=nn.LeakyReLU(0.2)):
        super().__init__()
        self.embedding = nn.Embedding(n_symbols, channels)

        padding = (kernel_size - 1) // 2
        self.cnn = nn.ModuleList()
        for _ in range(depth):
            self.cnn.append(nn.Sequential(
                weight_norm(nn.Conv1d(channels, channels, kernel_size=kernel_size, padding=padding)),
                LayerNorm(channels),
                actv,
                nn.Dropout(0.2),
            ))
        # self.cnn = nn.Sequential(*self.cnn)

        self.lstm = nn.LSTM(channels, channels // 2, 1, batch_first=True, bidirectional=True)

    def forward(self, x, input_lengths, m):
        x = self.embedding(x)  # [B, T, emb]
        x = x.transpose(1, 2)  # [B, emb, T]
        m = m.to(input_lengths.device).unsqueeze(1)
        x.masked_fill_(m, 0.0)

        for c in self.cnn:
            x = c(x)
            x.masked_fill_(m, 0.0)

        x = x.transpose(1, 2)  # [B, T, chn]

        input_lengths = input_lengths.cpu().numpy()
        x = nn.utils.rnn.pack_padded_sequence(
            x, input_lengths, batch_first=True, enforce_sorted=False)

        self.lstm.flatten_parameters()
        x, _ = self.lstm(x)
        x, _ = nn.utils.rnn.pad_packed_sequence(
            x, batch_first=True)

        x = x.transpose(-1, -2)
        x_pad = torch.zeros([x.shape[0], x.shape[1], m.shape[-1]])

        x_pad[:, :, :x.shape[-1]] = x
        x = x_pad.to(x.device)

        x.masked_fill_(m, 0.0)

        return x

    def inference(self, x):
        x = self.embedding(x)
        x = x.transpose(1, 2)
        x = self.cnn(x)
        x = x.transpose(1, 2)
        self.lstm.flatten_parameters()
        x, _ = self.lstm(x)
        return x

    def length_to_mask(self, lengths):
        mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths)
        mask = torch.gt(mask + 1, lengths.unsqueeze(1))
        return mask


class AdaIN1d(nn.Module):
    def __init__(self, style_dim, num_features):
        super().__init__()
        self.norm = nn.InstanceNorm1d(num_features, affine=False)
        self.fc = nn.Linear(style_dim, num_features * 2)

    def forward(self, x, s):
        h = self.fc(s)
        h = h.view(h.size(0), h.size(1), 1)
        gamma, beta = torch.chunk(h, chunks=2, dim=1)
        return (1 + gamma) * self.norm(x) + beta


class UpSample1d(nn.Module):
    def __init__(self, layer_type):
        super().__init__()
        self.layer_type = layer_type

    def forward(self, x):
        if self.layer_type == 'none':
            return x
        else:
            return F.interpolate(x, scale_factor=2, mode='nearest')


class AdainResBlk1d(nn.Module):
    def __init__(self, dim_in, dim_out, style_dim=64, actv=nn.LeakyReLU(0.2),
                 upsample='none', dropout_p=0.0):
        super().__init__()
        self.actv = actv
        self.upsample_type = upsample
        self.upsample = UpSample1d(upsample)
        self.learned_sc = dim_in != dim_out
        self._build_weights(dim_in, dim_out, style_dim)
        self.dropout = nn.Dropout(dropout_p)

        if upsample == 'none':
            self.pool = nn.Identity()
        else:
            self.pool = weight_norm(
                nn.ConvTranspose1d(dim_in, dim_in, kernel_size=3, stride=2, groups=dim_in, padding=1, output_padding=1))

    def _build_weights(self, dim_in, dim_out, style_dim):
        self.conv1 = weight_norm(nn.Conv1d(dim_in, dim_out, 3, 1, 1))
        self.conv2 = weight_norm(nn.Conv1d(dim_out, dim_out, 3, 1, 1))
        self.norm1 = AdaIN1d(style_dim, dim_in)
        self.norm2 = AdaIN1d(style_dim, dim_out)
        if self.learned_sc:
            self.conv1x1 = weight_norm(nn.Conv1d(dim_in, dim_out, 1, 1, 0, bias=False))

    def _shortcut(self, x):
        x = self.upsample(x)
        if self.learned_sc:
            x = self.conv1x1(x)
        return x

    def _residual(self, x, s):
        x = self.norm1(x, s)
        x = self.actv(x)
        x = self.pool(x)
        x = self.conv1(self.dropout(x))
        x = self.norm2(x, s)
        x = self.actv(x)
        x = self.conv2(self.dropout(x))
        return x

    def forward(self, x, s):
        out = self._residual(x, s)
        out = (out + self._shortcut(x)) / math.sqrt(2)
        return out


class Decoder(nn.Module):
    def __init__(self, dim_in=512, style_dim=64, residual_dim=64, dim_out=80):
        super().__init__()

        self.decode = nn.ModuleList()

        self.bottleneck_dim = dim_in * 2

        self.encode = nn.Sequential(ResBlk1d(dim_in + 2, self.bottleneck_dim, normalize=True),
                                    ResBlk1d(self.bottleneck_dim, self.bottleneck_dim, normalize=True))

        self.decode.append(AdainResBlk1d(self.bottleneck_dim + residual_dim + 2, self.bottleneck_dim, style_dim))
        self.decode.append(AdainResBlk1d(self.bottleneck_dim + residual_dim + 2, self.bottleneck_dim, style_dim))
        self.decode.append(AdainResBlk1d(self.bottleneck_dim + residual_dim + 2, dim_in, style_dim, upsample=True))
        self.decode.append(AdainResBlk1d(dim_in, dim_in, style_dim))
        self.decode.append(AdainResBlk1d(dim_in, dim_in, style_dim))

        self.F0_conv = nn.Sequential(
            ResBlk1d(1, residual_dim, normalize=True, downsample=True),
            weight_norm(nn.Conv1d(residual_dim, 1, kernel_size=1)),
            nn.InstanceNorm1d(1, affine=True)
        )

        self.N_conv = nn.Sequential(
            ResBlk1d(1, residual_dim, normalize=True, downsample=True),
            weight_norm(nn.Conv1d(residual_dim, 1, kernel_size=1)),
            nn.InstanceNorm1d(1, affine=True)
        )

        self.asr_res = nn.Sequential(
            weight_norm(nn.Conv1d(dim_in, residual_dim, kernel_size=1)),
            nn.InstanceNorm1d(residual_dim, affine=True)
        )

        self.to_out = nn.Sequential(weight_norm(nn.Conv1d(dim_in, dim_out, 1, 1, 0)))

    def forward(self, asr, F0, N, s):
        F0 = self.F0_conv(F0.unsqueeze(1))
        N = self.N_conv(N.unsqueeze(1))

        x = torch.cat([asr, F0, N], axis=1)
        x = self.encode(x)

        asr_res = self.asr_res(asr)

        res = True
        for block in self.decode:
            if res:
                x = torch.cat([x, asr_res, F0, N], axis=1)
            x = block(x, s)
            if block.upsample_type != "none":
                res = False

        x = self.to_out(x)
        return x


class AdaLayerNorm(nn.Module):
    def __init__(self, style_dim, channels, eps=1e-5):
        super().__init__()
        self.channels = channels
        self.eps = eps

        self.fc = nn.Linear(style_dim, channels * 2)

    def forward(self, x, s):
        x = x.transpose(-1, -2)
        x = x.transpose(1, -1)

        h = self.fc(s)
        h = h.view(h.size(0), h.size(1), 1)
        gamma, beta = torch.chunk(h, chunks=2, dim=1)
        gamma, beta = gamma.transpose(1, -1), beta.transpose(1, -1)

        x = F.layer_norm(x, (self.channels,), eps=self.eps)
        x = (1 + gamma) * x + beta
        return x.transpose(1, -1).transpose(-1, -2)


class LinearNorm(torch.nn.Module):
    def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
        super(LinearNorm, self).__init__()
        self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)

        torch.nn.init.xavier_uniform_(
            self.linear_layer.weight,
            gain=torch.nn.init.calculate_gain(w_init_gain))

    def forward(self, x):
        return self.linear_layer(x)


class ProsodyPredictor(nn.Module):

    def __init__(self, style_dim, d_hid, nlayers, dropout=0.1):
        super().__init__()

        self.text_encoder = DurationEncoder(sty_dim=style_dim,
                                            d_model=d_hid,
                                            nlayers=nlayers,
                                            dropout=dropout)

        self.lstm = nn.LSTM(d_hid + style_dim, d_hid // 2, 1, batch_first=True, bidirectional=True)
        self.duration_proj = LinearNorm(d_hid, 1)

        self.shared = nn.LSTM(d_hid + style_dim, d_hid // 2, 1, batch_first=True, bidirectional=True)
        self.F0 = nn.ModuleList()
        self.F0.append(AdainResBlk1d(d_hid, d_hid, style_dim, dropout_p=dropout))
        self.F0.append(AdainResBlk1d(d_hid, d_hid // 2, style_dim, upsample=True, dropout_p=dropout))
        self.F0.append(AdainResBlk1d(d_hid // 2, d_hid // 2, style_dim, dropout_p=dropout))

        self.N = nn.ModuleList()
        self.N.append(AdainResBlk1d(d_hid, d_hid, style_dim, dropout_p=dropout))
        self.N.append(AdainResBlk1d(d_hid, d_hid // 2, style_dim, upsample=True, dropout_p=dropout))
        self.N.append(AdainResBlk1d(d_hid // 2, d_hid // 2, style_dim, dropout_p=dropout))

        self.F0_proj = nn.Conv1d(d_hid // 2, 1, 1, 1, 0)
        self.N_proj = nn.Conv1d(d_hid // 2, 1, 1, 1, 0)

    def forward(self, texts, style, text_lengths, alignment, m):
        d = self.text_encoder(texts, style, text_lengths, m)

        batch_size = d.shape[0]
        text_size = d.shape[1]

        # predict duration
        input_lengths = text_lengths.cpu().numpy()
        x = nn.utils.rnn.pack_padded_sequence(
            d, input_lengths, batch_first=True, enforce_sorted=False)

        m = m.to(text_lengths.device).unsqueeze(1)

        self.lstm.flatten_parameters()
        x, _ = self.lstm(x)
        x, _ = nn.utils.rnn.pad_packed_sequence(
            x, batch_first=True)

        x_pad = torch.zeros([x.shape[0], m.shape[-1], x.shape[-1]])

        x_pad[:, :x.shape[1], :] = x
        x = x_pad.to(x.device)

        duration = self.duration_proj(nn.functional.dropout(x, 0.5, training=self.training))

        en = (d.transpose(-1, -2) @ alignment)

        return duration.squeeze(-1), en

    def F0Ntrain(self, x, s):
        x, _ = self.shared(x.transpose(-1, -2))

        F0 = x.transpose(-1, -2)
        for block in self.F0:
            F0 = block(F0, s)
        F0 = self.F0_proj(F0)

        N = x.transpose(-1, -2)
        for block in self.N:
            N = block(N, s)
        N = self.N_proj(N)

        return F0.squeeze(1), N.squeeze(1)

    def length_to_mask(self, lengths):
        mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths)
        mask = torch.gt(mask + 1, lengths.unsqueeze(1))
        return mask


class DurationEncoder(nn.Module):

    def __init__(self, sty_dim, d_model, nlayers, dropout=0.1):
        super().__init__()
        self.lstms = nn.ModuleList()
        for _ in range(nlayers):
            self.lstms.append(nn.LSTM(d_model + sty_dim,
                                      d_model // 2,
                                      num_layers=1,
                                      batch_first=True,
                                      bidirectional=True,
                                      dropout=dropout))
            self.lstms.append(AdaLayerNorm(sty_dim, d_model))

        self.dropout = dropout
        self.d_model = d_model
        self.sty_dim = sty_dim

    def forward(self, x, style, text_lengths, m):
        masks = m.to(text_lengths.device)

        x = x.permute(2, 0, 1)
        s = style.expand(x.shape[0], x.shape[1], -1)
        x = torch.cat([x, s], axis=-1)
        x.masked_fill_(masks.unsqueeze(-1).transpose(0, 1), 0.0)

        x = x.transpose(0, 1)
        input_lengths = text_lengths.cpu().numpy()
        x = x.transpose(-1, -2)

        for block in self.lstms:
            if isinstance(block, AdaLayerNorm):
                x = block(x.transpose(-1, -2), style).transpose(-1, -2)
                x = torch.cat([x, s.permute(1, -1, 0)], axis=1)
                x.masked_fill_(masks.unsqueeze(-1).transpose(-1, -2), 0.0)
            else:
                x = x.transpose(-1, -2)
                x = nn.utils.rnn.pack_padded_sequence(
                    x, input_lengths, batch_first=True, enforce_sorted=False)
                block.flatten_parameters()
                x, _ = block(x)
                x, _ = nn.utils.rnn.pad_packed_sequence(
                    x, batch_first=True)
                x = F.dropout(x, p=self.dropout, training=self.training)
                x = x.transpose(-1, -2)

                x_pad = torch.zeros([x.shape[0], x.shape[1], m.shape[-1]])

                x_pad[:, :, :x.shape[-1]] = x
                x = x_pad.to(x.device)

        return x.transpose(-1, -2)

    def inference(self, x, style):
        x = self.embedding(x.transpose(-1, -2)) * math.sqrt(self.d_model)
        style = style.expand(x.shape[0], x.shape[1], -1)
        x = torch.cat([x, style], axis=-1)
        src = self.pos_encoder(x)
        output = self.transformer_encoder(src).transpose(0, 1)
        return output

    def length_to_mask(self, lengths):
        mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths)
        mask = torch.gt(mask + 1, lengths.unsqueeze(1))
        return mask


def load_F0_models(path):
    # load F0 model

    F0_model = JDCNet(num_class=1, seq_len=192)
    params = torch.load(path, map_location='cpu')['net']
    F0_model.load_state_dict(params)
    _ = F0_model.train()

    return F0_model


def load_ASR_models(ASR_MODEL_PATH, ASR_MODEL_CONFIG):
    # load ASR model
    def _load_config(path):
        with open(path) as f:
            config = yaml.safe_load(f)
        model_config = config['model_params']
        return model_config

    def _load_model(model_config, model_path):
        model = ASRCNN(**model_config)
        params = torch.load(model_path, map_location='cpu')['model']
        model.load_state_dict(params)
        return model

    asr_model_config = _load_config(ASR_MODEL_CONFIG)
    asr_model = _load_model(asr_model_config, ASR_MODEL_PATH)
    _ = asr_model.train()

    return asr_model


def build_model(args, text_aligner, pitch_extractor):
    decoder = Decoder(dim_in=args.hidden_dim, style_dim=args.style_dim, dim_out=args.n_mels)

    text_encoder = TextEncoder(channels=args.hidden_dim, kernel_size=5, depth=args.n_layer, n_symbols=args.n_token)

    predictor = ProsodyPredictor(style_dim=args.style_dim, d_hid=args.hidden_dim, nlayers=args.n_layer,
                                 dropout=args.dropout)

    style_encoder = StyleEncoder(dim_in=args.dim_in, style_dim=args.style_dim, max_conv_dim=args.hidden_dim)

    predictor_encoder = StyleEncoder(dim_in=args.dim_in, style_dim=args.style_dim,
                                     max_conv_dim=args.hidden_dim)

    discriminator = Discriminator2d(dim_in=args.dim_in, num_domains=1, max_conv_dim=args.hidden_dim)

    # define diffusion model
    if args.multispeaker:
        transformer = StyleTransformer1d(channels=args.style_dim * 2,
                                         context_embedding_features=args.hidden_dim, # TODO: Figure out embeddings
                                         context_features=args.style_dim * 2,
                                         **args.diffusion.transformer)
    else:
        transformer = Transformer1d(channels=args.style_dim * 2,
                                    context_embedding_features=args.hidden_dim, # TODO: Figure out embeddings
                                    **args.diffusion.transformer)

    diffusion = AudioDiffusionConditional(
        in_channels=1,
        embedding_max_length=args.diffusion.diff_embedding_max_length, # TODO: Figure out embeddings
        embedding_features=args.hidden_dim, # TODO: Figure out embeddings
        embedding_mask_proba=args.diffusion.embedding_mask_proba,  # Conditional dropout of batch elements,
        channels=args.style_dim * 2,
        context_features=args.style_dim * 2,
    )

    diffusion.diffusion = KDiffusion(
        net=diffusion.unet,
        sigma_distribution=LogNormalDistribution(mean=args.diffusion.dist.mean, std=args.diffusion.dist.std),
        sigma_data=args.diffusion.dist.sigma_data,
        # a placeholder, will be changed dynamically when start training diffusion model
        dynamic_threshold=0.0
    )
    diffusion.diffusion.net = transformer
    diffusion.unet = transformer

    nets = Munch(predictor=predictor,
                 decoder=decoder,
                 text_encoder=text_encoder,

                 predictor_encoder=predictor_encoder,
                 style_encoder=style_encoder,
                 diffusion=diffusion,


                 pitch_extractor=pitch_extractor,
                 text_aligner=text_aligner,

                 discriminator=discriminator)
    return nets


def load_checkpoint(model, optimizer, path, load_only_params=True, ignore_modules=[]):
    state = torch.load(path, map_location='cpu')
    params = state['net']

    # for key in model:
    #    from collections import OrderedDict
    #    new_state_dict = OrderedDict()
    #    for k,v in params[key].items(): # Fix for non-distributed training
    #        if not k.startswith("module"):
    #            #print(f"load_checkpoint: {k}")
    #            name = 'module.' + k
    #        else:
    #            name = k
    #        new_state_dict[name] = v

    #    if key in ['mpd', 'msd', 'wd']:
    #        new_state_dict = params[key]

    #    if key in params and key not in ignore_modules:
    #        print('%s loaded' % key)
    #        model[key].load_state_dict(new_state_dict)
    #        #model[key].load_state_dict(params[key], strict=False)

    for key in model:
        if key in params and key not in ignore_modules:
            try:
                model[key].load_state_dict(params[key], strict=True)
            except:
                from collections import OrderedDict
                state_dict = params[key]
                new_state_dict = OrderedDict()
                print(
                    f'{key} key length: {len(model[key].state_dict().keys())}, state_dict key length: {len(state_dict.keys())}')
                for (k_m, v_m), (k_c, v_c) in zip(model[key].state_dict().items(), state_dict.items()):
                    new_state_dict[k_m] = v_c

                model[key].load_state_dict(new_state_dict, strict=False)
            print('%s loaded' % key)

    if not load_only_params:
        epoch = state["epoch"]
        iters = state["iters"]
        optimizer.load_state_dict(state["optimizer"])
    else:
        epoch = 0
        iters = 0

    return model, optimizer, epoch, iters