import math
import torch
from torch import nn
from torch.nn import Parameter
import torch.nn.functional as F
import numpy as np
class StyleAdaptiveLayerNorm(nn.Module):
def __init__(self, normalized_shape, eps=1e-5):
super().__init__()
self.in_dim = normalized_shape
self.norm = nn.LayerNorm(self.in_dim, eps=eps, elementwise_affine=False)
self.style = nn.Linear(self.in_dim, self.in_dim * 2)
self.style.bias.data[: self.in_dim] = 1
self.style.bias.data[self.in_dim :] = 0
def forward(self, x, condition):
# x: (B, T, d); condition: (B, T, d)
style = self.style(torch.mean(condition, dim=1, keepdim=True))
gamma, beta = style.chunk(2, -1)
out = self.norm(x)
out = gamma * out + beta
return out
class PositionalEncoding(nn.Module):
def __init__(self, d_model, dropout, max_len=5000):
super().__init__()
self.dropout = dropout
position = torch.arange(max_len).unsqueeze(1)
div_term = torch.exp(
torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model)
)
pe = torch.zeros(max_len, 1, d_model)
pe[:, 0, 0::2] = torch.sin(position * div_term)
pe[:, 0, 1::2] = torch.cos(position * div_term)
self.register_buffer("pe", pe)
def forward(self, x):
x = x + self.pe[: x.size(0)]
return F.dropout(x, self.dropout, training=self.training)
class TransformerFFNLayer(nn.Module):
def __init__(
self, encoder_hidden, conv_filter_size, conv_kernel_size, encoder_dropout
):
super().__init__()
self.encoder_hidden = encoder_hidden
self.conv_filter_size = conv_filter_size
self.conv_kernel_size = conv_kernel_size
self.encoder_dropout = encoder_dropout
self.ffn_1 = nn.Conv1d(
self.encoder_hidden,
self.conv_filter_size,
self.conv_kernel_size,
padding=self.conv_kernel_size // 2,
)
self.ffn_1.weight.data.normal_(0.0, 0.02)
self.ffn_2 = nn.Linear(self.conv_filter_size, self.encoder_hidden)
self.ffn_2.weight.data.normal_(0.0, 0.02)
def forward(self, x):
# x: (B, T, d)
x = self.ffn_1(x.permute(0, 2, 1)).permute(
0, 2, 1
) # (B, T, d) -> (B, d, T) -> (B, T, d)
x = F.relu(x)
x = F.dropout(x, self.encoder_dropout, training=self.training)
x = self.ffn_2(x)
return x
class TransformerEncoderLayer(nn.Module):
def __init__(
self,
encoder_hidden,
encoder_head,
conv_filter_size,
conv_kernel_size,
encoder_dropout,
use_cln,
):
super().__init__()
self.encoder_hidden = encoder_hidden
self.encoder_head = encoder_head
self.conv_filter_size = conv_filter_size
self.conv_kernel_size = conv_kernel_size
self.encoder_dropout = encoder_dropout
self.use_cln = use_cln
if not self.use_cln:
self.ln_1 = nn.LayerNorm(self.encoder_hidden)
self.ln_2 = nn.LayerNorm(self.encoder_hidden)
else:
self.ln_1 = StyleAdaptiveLayerNorm(self.encoder_hidden)
self.ln_2 = StyleAdaptiveLayerNorm(self.encoder_hidden)
self.self_attn = nn.MultiheadAttention(
self.encoder_hidden, self.encoder_head, batch_first=True
)
self.ffn = TransformerFFNLayer(
self.encoder_hidden,
self.conv_filter_size,
self.conv_kernel_size,
self.encoder_dropout,
)
def forward(self, x, key_padding_mask, conditon=None):
# x: (B, T, d); key_padding_mask: (B, T), mask is 0; condition: (B, T, d)
# self attention
residual = x
if self.use_cln:
x = self.ln_1(x, conditon)
else:
x = self.ln_1(x)
if key_padding_mask != None:
key_padding_mask_input = ~(key_padding_mask.bool())
else:
key_padding_mask_input = None
x, _ = self.self_attn(
query=x, key=x, value=x, key_padding_mask=key_padding_mask_input
)
x = F.dropout(x, self.encoder_dropout, training=self.training)
x = residual + x
# ffn
residual = x
if self.use_cln:
x = self.ln_2(x, conditon)
else:
x = self.ln_2(x)
x = self.ffn(x)
x = residual + x
return x
class TransformerEncoder(nn.Module):
def __init__(
self,
enc_emb_tokens=None,
encoder_layer=None,
encoder_hidden=None,
encoder_head=None,
conv_filter_size=None,
conv_kernel_size=None,
encoder_dropout=None,
use_cln=None,
cfg=None,
):
super().__init__()
self.encoder_layer = (
encoder_layer if encoder_layer is not None else cfg.encoder_layer
)
self.encoder_hidden = (
encoder_hidden if encoder_hidden is not None else cfg.encoder_hidden
)
self.encoder_head = (
encoder_head if encoder_head is not None else cfg.encoder_head
)
self.conv_filter_size = (
conv_filter_size if conv_filter_size is not None else cfg.conv_filter_size
)
self.conv_kernel_size = (
conv_kernel_size if conv_kernel_size is not None else cfg.conv_kernel_size
)
self.encoder_dropout = (
encoder_dropout if encoder_dropout is not None else cfg.encoder_dropout
)
self.use_cln = use_cln if use_cln is not None else cfg.use_cln
if enc_emb_tokens != None:
self.use_enc_emb = True
self.enc_emb_tokens = enc_emb_tokens
else:
self.use_enc_emb = False
self.position_emb = PositionalEncoding(
self.encoder_hidden, self.encoder_dropout
)
self.layers = nn.ModuleList([])
self.layers.extend(
[
TransformerEncoderLayer(
self.encoder_hidden,
self.encoder_head,
self.conv_filter_size,
self.conv_kernel_size,
self.encoder_dropout,
self.use_cln,
)
for i in range(self.encoder_layer)
]
)
if self.use_cln:
self.last_ln = StyleAdaptiveLayerNorm(self.encoder_hidden)
else:
self.last_ln = nn.LayerNorm(self.encoder_hidden)
def forward(self, x, key_padding_mask, condition=None):
if len(x.shape) == 2 and self.use_enc_emb:
x = self.enc_emb_tokens(x)
x = self.position_emb(x)
else:
x = self.position_emb(x) # (B, T, d)
for layer in self.layers:
x = layer(x, key_padding_mask, condition)
if self.use_cln:
x = self.last_ln(x, condition)
else:
x = self.last_ln(x)
return x
class DurationPredictor(nn.Module):
def __init__(self, cfg):
super().__init__()
self.cfg = cfg
self.input_size = cfg.input_size
self.filter_size = cfg.filter_size
self.kernel_size = cfg.kernel_size
self.conv_layers = cfg.conv_layers
self.cross_attn_per_layer = cfg.cross_attn_per_layer
self.attn_head = cfg.attn_head
self.drop_out = cfg.drop_out
self.conv = nn.ModuleList()
self.cattn = nn.ModuleList()
for idx in range(self.conv_layers):
in_dim = self.input_size if idx == 0 else self.filter_size
self.conv += [
nn.Sequential(
nn.Conv1d(
in_dim,
self.filter_size,
self.kernel_size,
padding=self.kernel_size // 2,
),
nn.ReLU(),
nn.LayerNorm(self.filter_size),
nn.Dropout(self.drop_out),
)
]
if idx % self.cross_attn_per_layer == 0:
self.cattn.append(
torch.nn.Sequential(
nn.MultiheadAttention(
self.filter_size,
self.attn_head,
batch_first=True,
kdim=self.filter_size,
vdim=self.filter_size,
),
nn.LayerNorm(self.filter_size),
nn.Dropout(0.2),
)
)
self.linear = nn.Linear(self.filter_size, 1)
self.linear.weight.data.normal_(0.0, 0.02)
def forward(self, x, mask, ref_emb, ref_mask):
"""
input:
x: (B, N, d)
mask: (B, N), mask is 0
ref_emb: (B, d, T')
ref_mask: (B, T'), mask is 0
output:
dur_pred: (B, N)
dur_pred_log: (B, N)
dur_pred_round: (B, N)
"""
input_ref_mask = ~(ref_mask.bool()) # (B, T')
# print(input_ref_mask)
x = x.transpose(1, -1) # (B, N, d) -> (B, d, N)
for idx, (conv, act, ln, dropout) in enumerate(self.conv):
res = x
# print(torch.min(x), torch.max(x))
if idx % self.cross_attn_per_layer == 0:
attn_idx = idx // self.cross_attn_per_layer
attn, attn_ln, attn_drop = self.cattn[attn_idx]
attn_res = y_ = x.transpose(1, 2) # (B, d, N) -> (B, N, d)
y_ = attn_ln(y_)
# print(torch.min(y_), torch.min(y_))
# print(torch.min(ref_emb), torch.max(ref_emb))
y_, _ = attn(
y_,
ref_emb.transpose(1, 2),
ref_emb.transpose(1, 2),
key_padding_mask=input_ref_mask,
)
# y_, _ = attn(y_, ref_emb.transpose(1, 2), ref_emb.transpose(1, 2))
# print(torch.min(y_), torch.min(y_))
y_ = attn_drop(y_)
y_ = (y_ + attn_res) / math.sqrt(2.0)
x = y_.transpose(1, 2)
x = conv(x)
# print(torch.min(x), torch.max(x))
x = act(x)
x = ln(x.transpose(1, 2))
# print(torch.min(x), torch.max(x))
x = x.transpose(1, 2)
x = dropout(x)
if idx != 0:
x += res
if mask is not None:
x = x * mask.to(x.dtype)[:, None, :]
x = self.linear(x.transpose(1, 2))
x = torch.squeeze(x, -1)
dur_pred = x.exp() - 1
dur_pred_round = torch.clamp(torch.round(x.exp() - 1), min=0).long()
return {
"dur_pred_log": x,
"dur_pred": dur_pred,
"dur_pred_round": dur_pred_round,
}
class PitchPredictor(nn.Module):
def __init__(self, cfg):
super().__init__()
self.cfg = cfg
self.input_size = cfg.input_size
self.filter_size = cfg.filter_size
self.kernel_size = cfg.kernel_size
self.conv_layers = cfg.conv_layers
self.cross_attn_per_layer = cfg.cross_attn_per_layer
self.attn_head = cfg.attn_head
self.drop_out = cfg.drop_out
self.conv = nn.ModuleList()
self.cattn = nn.ModuleList()
for idx in range(self.conv_layers):
in_dim = self.input_size if idx == 0 else self.filter_size
self.conv += [
nn.Sequential(
nn.Conv1d(
in_dim,
self.filter_size,
self.kernel_size,
padding=self.kernel_size // 2,
),
nn.ReLU(),
nn.LayerNorm(self.filter_size),
nn.Dropout(self.drop_out),
)
]
if idx % self.cross_attn_per_layer == 0:
self.cattn.append(
torch.nn.Sequential(
nn.MultiheadAttention(
self.filter_size,
self.attn_head,
batch_first=True,
kdim=self.filter_size,
vdim=self.filter_size,
),
nn.LayerNorm(self.filter_size),
nn.Dropout(0.2),
)
)
self.linear = nn.Linear(self.filter_size, 1)
self.linear.weight.data.normal_(0.0, 0.02)
def forward(self, x, mask, ref_emb, ref_mask):
"""
input:
x: (B, N, d)
mask: (B, N), mask is 0
ref_emb: (B, d, T')
ref_mask: (B, T'), mask is 0
output:
pitch_pred: (B, T)
"""
input_ref_mask = ~(ref_mask.bool()) # (B, T')
x = x.transpose(1, -1) # (B, N, d) -> (B, d, N)
for idx, (conv, act, ln, dropout) in enumerate(self.conv):
res = x
if idx % self.cross_attn_per_layer == 0:
attn_idx = idx // self.cross_attn_per_layer
attn, attn_ln, attn_drop = self.cattn[attn_idx]
attn_res = y_ = x.transpose(1, 2) # (B, d, N) -> (B, N, d)
y_ = attn_ln(y_)
y_, _ = attn(
y_,
ref_emb.transpose(1, 2),
ref_emb.transpose(1, 2),
key_padding_mask=input_ref_mask,
)
# y_, _ = attn(y_, ref_emb.transpose(1, 2), ref_emb.transpose(1, 2))
y_ = attn_drop(y_)
y_ = (y_ + attn_res) / math.sqrt(2.0)
x = y_.transpose(1, 2)
x = conv(x)
x = act(x)
x = ln(x.transpose(1, 2))
x = x.transpose(1, 2)
x = dropout(x)
if idx != 0:
x += res
x = self.linear(x.transpose(1, 2))
x = torch.squeeze(x, -1)
return x
def pad(input_ele, mel_max_length=None):
if mel_max_length:
max_len = mel_max_length
else:
max_len = max([input_ele[i].size(0) for i in range(len(input_ele))])
out_list = list()
for i, batch in enumerate(input_ele):
if len(batch.shape) == 1:
one_batch_padded = F.pad(
batch, (0, max_len - batch.size(0)), "constant", 0.0
)
elif len(batch.shape) == 2:
one_batch_padded = F.pad(
batch, (0, 0, 0, max_len - batch.size(0)), "constant", 0.0
)
out_list.append(one_batch_padded)
out_padded = torch.stack(out_list)
return out_padded
class LengthRegulator(nn.Module):
"""Length Regulator"""
def __init__(self):
super(LengthRegulator, self).__init__()
def LR(self, x, duration, max_len):
device = x.device
output = list()
mel_len = list()
for batch, expand_target in zip(x, duration):
expanded = self.expand(batch, expand_target)
output.append(expanded)
mel_len.append(expanded.shape[0])
if max_len is not None:
output = pad(output, max_len)
else:
output = pad(output)
return output, torch.LongTensor(mel_len).to(device)
def expand(self, batch, predicted):
out = list()
for i, vec in enumerate(batch):
expand_size = predicted[i].item()
out.append(vec.expand(max(int(expand_size), 0), -1))
out = torch.cat(out, 0)
return out
def forward(self, x, duration, max_len):
output, mel_len = self.LR(x, duration, max_len)
return output, mel_len