# 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
import math
import torch
from torch import nn
from torch.nn import functional as F
import commons
import modules
import attentions
from torch.nn import Conv1d, ConvTranspose1d, Conv2d
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
from commons import init_weights, get_padding
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
class TextEncoderOpenVoice(nn.Module):
def __init__(self,
n_vocab,
out_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout):
super().__init__()
self.n_vocab = n_vocab
self.out_channels = out_channels
self.hidden_channels = hidden_channels
self.filter_channels = filter_channels
self.n_heads = n_heads
self.n_layers = n_layers
self.kernel_size = kernel_size
self.p_dropout = p_dropout
self.emb = nn.Embedding(n_vocab, hidden_channels)
nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
self.encoder = attentions.Encoder(
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout)
self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1)
def forward(self, x, x_lengths):
x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
x = torch.transpose(x, 1, -1) # [b, h, t]
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
x = self.encoder(x * x_mask, x_mask)
stats = self.proj(x) * x_mask
m, logs = torch.split(stats, self.out_channels, dim=1)
return x, m, logs, x_mask
class DurationPredictor(nn.Module):
def __init__(
self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
):
super().__init__()
self.in_channels = in_channels
self.filter_channels = filter_channels
self.kernel_size = kernel_size
self.p_dropout = p_dropout
self.gin_channels = gin_channels
self.drop = nn.Dropout(p_dropout)
self.conv_1 = nn.Conv1d(
in_channels, filter_channels, kernel_size, padding=kernel_size // 2
)
self.norm_1 = modules.LayerNorm(filter_channels)
self.conv_2 = nn.Conv1d(
filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
)
self.norm_2 = modules.LayerNorm(filter_channels)
self.proj = nn.Conv1d(filter_channels, 1, 1)
if gin_channels != 0:
self.cond = nn.Conv1d(gin_channels, in_channels, 1)
def forward(self, x, x_mask, g=None):
x = torch.detach(x)
if g is not None:
g = torch.detach(g)
x = x + self.cond(g)
x = self.conv_1(x * x_mask)
x = torch.relu(x)
x = self.norm_1(x)
x = self.drop(x)
x = self.conv_2(x * x_mask)
x = torch.relu(x)
x = self.norm_2(x)
x = self.drop(x)
x = self.proj(x * x_mask)
return x * x_mask
class StochasticDurationPredictor(nn.Module):
def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
super().__init__()
filter_channels = in_channels # it needs to be removed from future version.
self.in_channels = in_channels
self.filter_channels = filter_channels
self.kernel_size = kernel_size
self.p_dropout = p_dropout
self.n_flows = n_flows
self.gin_channels = gin_channels
self.log_flow = modules.Log()
self.flows = nn.ModuleList()
self.flows.append(modules.ElementwiseAffine(2))
for i in range(n_flows):
self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
self.flows.append(modules.Flip())
self.post_pre = nn.Conv1d(1, filter_channels, 1)
self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
self.post_flows = nn.ModuleList()
self.post_flows.append(modules.ElementwiseAffine(2))
for i in range(4):
self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
self.post_flows.append(modules.Flip())
self.pre = nn.Conv1d(in_channels, filter_channels, 1)
self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
if gin_channels != 0:
self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
x = torch.detach(x)
x = self.pre(x)
if g is not None:
g = torch.detach(g)
x = x + self.cond(g)
x = self.convs(x, x_mask)
x = self.proj(x) * x_mask
if not reverse:
flows = self.flows
assert w is not None
logdet_tot_q = 0
h_w = self.post_pre(w)
h_w = self.post_convs(h_w, x_mask)
h_w = self.post_proj(h_w) * x_mask
e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
z_q = e_q
for flow in self.post_flows:
z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
logdet_tot_q += logdet_q
z_u, z1 = torch.split(z_q, [1, 1], 1)
u = torch.sigmoid(z_u) * x_mask
z0 = (w - u) * x_mask
logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2])
logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q
logdet_tot = 0
z0, logdet = self.log_flow(z0, x_mask)
logdet_tot += logdet
z = torch.cat([z0, z1], 1)
for flow in flows:
z, logdet = flow(z, x_mask, g=x, reverse=reverse)
logdet_tot = logdet_tot + logdet
nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot
return nll + logq # [b]
else:
flows = list(reversed(self.flows))
flows = flows[:-2] + [flows[-1]] # remove a useless vflow
z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
for flow in flows:
z = flow(z, x_mask, g=x, reverse=reverse)
z0, z1 = torch.split(z, [1, 1], 1)
logw = z0
return logw
class PosteriorEncoder(nn.Module):
def __init__(
self,
in_channels,
out_channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=0,
):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.hidden_channels = hidden_channels
self.kernel_size = kernel_size
self.dilation_rate = dilation_rate
self.n_layers = n_layers
self.gin_channels = gin_channels
self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
self.enc = modules.WN(
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=gin_channels,
)
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
def forward(self, x, x_lengths, g=None, tau=1.0):
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
x.dtype
)
x = self.pre(x) * x_mask
x = self.enc(x, x_mask, g=g)
stats = self.proj(x) * x_mask
m, logs = torch.split(stats, self.out_channels, dim=1)
z = (m + torch.randn_like(m) * tau * torch.exp(logs)) * x_mask
return z, m, logs, x_mask
class Generator(torch.nn.Module):
def __init__(
self,
initial_channel,
resblock,
resblock_kernel_sizes,
resblock_dilation_sizes,
upsample_rates,
upsample_initial_channel,
upsample_kernel_sizes,
gin_channels=0,
):
super(Generator, self).__init__()
self.num_kernels = len(resblock_kernel_sizes)
self.num_upsamples = len(upsample_rates)
self.conv_pre = Conv1d(
initial_channel, upsample_initial_channel, 7, 1, padding=3
)
resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
self.ups = nn.ModuleList()
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
self.ups.append(
weight_norm(
ConvTranspose1d(
upsample_initial_channel // (2**i),
upsample_initial_channel // (2 ** (i + 1)),
k,
u,
padding=(k - u) // 2,
)
)
)
self.resblocks = nn.ModuleList()
for i in range(len(self.ups)):
ch = upsample_initial_channel // (2 ** (i + 1))
for j, (k, d) in enumerate(
zip(resblock_kernel_sizes, resblock_dilation_sizes)
):
self.resblocks.append(resblock(ch, k, d))
self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
self.ups.apply(init_weights)
if gin_channels != 0:
self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
def forward(self, x, g=None):
x = self.conv_pre(x)
if g is not None:
x = x + self.cond(g)
for i in range(self.num_upsamples):
x = F.leaky_relu(x, modules.LRELU_SLOPE)
x = self.ups[i](x)
xs = None
for j in range(self.num_kernels):
if xs is None:
xs = self.resblocks[i * self.num_kernels + j](x)
else:
xs += self.resblocks[i * self.num_kernels + j](x)
x = xs / self.num_kernels
x = F.leaky_relu(x)
x = self.conv_post(x)
x = torch.tanh(x)
return x
def remove_weight_norm(self):
print("Removing weight norm...")
for layer in self.ups:
remove_weight_norm(layer)
for layer in self.resblocks:
layer.remove_weight_norm()
class ReferenceEncoder(nn.Module):
"""
inputs --- [N, Ty/r, n_mels*r] mels
outputs --- [N, ref_enc_gru_size]
"""
def __init__(self, spec_channels, gin_channels=0, layernorm=True):
super().__init__()
self.spec_channels = spec_channels
ref_enc_filters = [32, 32, 64, 64, 128, 128]
K = len(ref_enc_filters)
filters = [1] + ref_enc_filters
convs = [
weight_norm(
nn.Conv2d(
in_channels=filters[i],
out_channels=filters[i + 1],
kernel_size=(3, 3),
stride=(2, 2),
padding=(1, 1),
)
)
for i in range(K)
]
self.convs = nn.ModuleList(convs)
out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K)
self.gru = nn.GRU(
input_size=ref_enc_filters[-1] * out_channels,
hidden_size=256 // 2,
batch_first=True,
)
self.proj = nn.Linear(128, gin_channels)
if layernorm:
self.layernorm = nn.LayerNorm(self.spec_channels)
else:
self.layernorm = None
def forward(self, inputs, mask=None):
N = inputs.size(0)
out = inputs.view(N, 1, -1, self.spec_channels) # [N, 1, Ty, n_freqs]
if self.layernorm is not None:
out = self.layernorm(out)
for conv in self.convs:
out = conv(out)
# out = wn(out)
out = F.relu(out) # [N, 128, Ty//2^K, n_mels//2^K]
out = out.transpose(1, 2) # [N, Ty//2^K, 128, n_mels//2^K]
T = out.size(1)
N = out.size(0)
out = out.contiguous().view(N, T, -1) # [N, Ty//2^K, 128*n_mels//2^K]
self.gru.flatten_parameters()
memory, out = self.gru(out) # out --- [1, N, 128]
return self.proj(out.squeeze(0))
def calculate_channels(self, L, kernel_size, stride, pad, n_convs):
for i in range(n_convs):
L = (L - kernel_size + 2 * pad) // stride + 1
return L
class ResidualCouplingBlock(nn.Module):
def __init__(self,
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
n_flows=4,
gin_channels=0):
super().__init__()
self.channels = channels
self.hidden_channels = hidden_channels
self.kernel_size = kernel_size
self.dilation_rate = dilation_rate
self.n_layers = n_layers
self.n_flows = n_flows
self.gin_channels = gin_channels
self.flows = nn.ModuleList()
for i in range(n_flows):
self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
self.flows.append(modules.Flip())
def forward(self, x, x_mask, g=None, reverse=False):
if not reverse:
for flow in self.flows:
x, _ = flow(x, x_mask, g=g, reverse=reverse)
else:
for flow in reversed(self.flows):
x = flow(x, x_mask, g=g, reverse=reverse)
return x
class SynthesizerTrn(nn.Module):
"""
Synthesizer for Training
"""
def __init__(
self,
n_vocab,
spec_channels,
inter_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
resblock,
resblock_kernel_sizes,
resblock_dilation_sizes,
upsample_rates,
upsample_initial_channel,
upsample_kernel_sizes,
n_speakers=256,
gin_channels=256,
**kwargs
):
super().__init__()
self.dec = Generator(
inter_channels,
resblock,
resblock_kernel_sizes,
resblock_dilation_sizes,
upsample_rates,
upsample_initial_channel,
upsample_kernel_sizes,
gin_channels=gin_channels,
)
self.enc_q = PosteriorEncoder(
spec_channels,
inter_channels,
hidden_channels,
5,
1,
16,
gin_channels=gin_channels,
)
self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
self.n_speakers = n_speakers
if n_speakers == 0:
self.ref_enc = ReferenceEncoder(spec_channels, gin_channels)
else:
self.enc_p = TextEncoderOpenVoice(n_vocab,
inter_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout)
self.sdp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
self.emb_g = nn.Embedding(n_speakers, gin_channels)
def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., sdp_ratio=0.2, max_len=None):
x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
if self.n_speakers > 0:
g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
else:
g = None
logw = self.sdp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) * sdp_ratio \
+ self.dp(x, x_mask, g=g) * (1 - sdp_ratio)
w = torch.exp(logw) * x_mask * length_scale
w_ceil = torch.ceil(w)
y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
attn = commons.generate_path(w_ceil, attn_mask)
m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
z = self.flow(z_p, y_mask, g=g, reverse=True)
o = self.dec((z * y_mask)[:,:,:max_len], g=g)
return o, attn, y_mask, (z, z_p, m_p, logs_p)
def voice_conversion(self, y, y_lengths, sid_src, sid_tgt, tau=1.0):
g_src = sid_src
g_tgt = sid_tgt
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src, tau=tau)
z_p = self.flow(z, y_mask, g=g_src)
z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
o_hat = self.dec(z_hat * y_mask, g=g_tgt)
return o_hat, y_mask, (z, z_p, z_hat)