styletts2 / models.py
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# 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 Modules.discriminators import (
MultiPeriodDiscriminator,
MultiResSpecDiscriminator,
WavLMDiscriminator,
)
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 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 ProsodyPredictor(nn.Module):
def __init__(self, style_dim, d_hid, nlayers, max_dur=50, 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, max_dur)
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, bert):
assert args.decoder.type in ["istftnet", "hifigan"], "Decoder type unknown"
if args.decoder.type == "istftnet":
from Modules.istftnet import Decoder
decoder = Decoder(
dim_in=args.hidden_dim,
style_dim=args.style_dim,
dim_out=args.n_mels,
resblock_kernel_sizes=args.decoder.resblock_kernel_sizes,
upsample_rates=args.decoder.upsample_rates,
upsample_initial_channel=args.decoder.upsample_initial_channel,
resblock_dilation_sizes=args.decoder.resblock_dilation_sizes,
upsample_kernel_sizes=args.decoder.upsample_kernel_sizes,
gen_istft_n_fft=args.decoder.gen_istft_n_fft,
gen_istft_hop_size=args.decoder.gen_istft_hop_size,
)
else:
from Modules.hifigan import Decoder
decoder = Decoder(
dim_in=args.hidden_dim,
style_dim=args.style_dim,
dim_out=args.n_mels,
resblock_kernel_sizes=args.decoder.resblock_kernel_sizes,
upsample_rates=args.decoder.upsample_rates,
upsample_initial_channel=args.decoder.upsample_initial_channel,
resblock_dilation_sizes=args.decoder.resblock_dilation_sizes,
upsample_kernel_sizes=args.decoder.upsample_kernel_sizes,
)
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,
max_dur=args.max_dur,
dropout=args.dropout,
)
style_encoder = StyleEncoder(
dim_in=args.dim_in, style_dim=args.style_dim, max_conv_dim=args.hidden_dim
) # acoustic style encoder
predictor_encoder = StyleEncoder(
dim_in=args.dim_in, style_dim=args.style_dim, max_conv_dim=args.hidden_dim
) # prosodic style encoder
# define diffusion model
if args.multispeaker:
transformer = StyleTransformer1d(
channels=args.style_dim * 2,
context_embedding_features=bert.config.hidden_size,
context_features=args.style_dim * 2,
**args.diffusion.transformer
)
else:
transformer = Transformer1d(
channels=args.style_dim * 2,
context_embedding_features=bert.config.hidden_size,
**args.diffusion.transformer
)
diffusion = AudioDiffusionConditional(
in_channels=1,
embedding_max_length=bert.config.max_position_embeddings,
embedding_features=bert.config.hidden_size,
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(
bert=bert,
bert_encoder=nn.Linear(bert.config.hidden_size, args.hidden_dim),
predictor=predictor,
decoder=decoder,
text_encoder=text_encoder,
predictor_encoder=predictor_encoder,
style_encoder=style_encoder,
diffusion=diffusion,
text_aligner=text_aligner,
pitch_extractor=pitch_extractor,
mpd=MultiPeriodDiscriminator(),
msd=MultiResSpecDiscriminator(),
# slm discriminator head
wd=WavLMDiscriminator(
args.slm.hidden, args.slm.nlayers, args.slm.initial_channel
),
)
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:
if key in params and key not in ignore_modules:
print("%s loaded" % key)
model[key].load_state_dict(params[key], strict=False)
_ = [model[key].eval() for key in model]
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