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# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
import math
import json5
from librosa.filters import mel as librosa_mel_fn
from einops.layers.torch import Rearrange
class Diffusion(nn.Module):
def __init__(self, cfg, diff_model):
super().__init__()
self.cfg = cfg
self.diff_estimator = diff_model
self.beta_min = cfg.beta_min
self.beta_max = cfg.beta_max
self.sigma = cfg.sigma
self.noise_factor = cfg.noise_factor
def forward(self, x, condition_embedding, x_mask, reference_embedding, offset=1e-5):
diffusion_step = torch.rand(
x.shape[0], dtype=x.dtype, device=x.device, requires_grad=False
)
diffusion_step = torch.clamp(diffusion_step, offset, 1.0 - offset)
xt, z = self.forward_diffusion(x0=x, diffusion_step=diffusion_step)
cum_beta = self.get_cum_beta(diffusion_step.unsqueeze(-1).unsqueeze(-1))
x0_pred = self.diff_estimator(
xt, condition_embedding, x_mask, reference_embedding, diffusion_step
)
mean_pred = x0_pred * torch.exp(-0.5 * cum_beta / (self.sigma**2))
variance = (self.sigma**2) * (1.0 - torch.exp(-cum_beta / (self.sigma**2)))
noise_pred = (xt - mean_pred) / (torch.sqrt(variance) * self.noise_factor)
noise = z
diff_out = {"x0_pred": x0_pred, "noise_pred": noise_pred, "noise": noise}
return diff_out
@torch.no_grad()
def get_cum_beta(self, time_step):
return self.beta_min * time_step + 0.5 * (self.beta_max - self.beta_min) * (
time_step**2
)
@torch.no_grad()
def get_beta_t(self, time_step):
return self.beta_min + (self.beta_max - self.beta_min) * time_step
@torch.no_grad()
def forward_diffusion(self, x0, diffusion_step):
time_step = diffusion_step.unsqueeze(-1).unsqueeze(-1)
cum_beta = self.get_cum_beta(time_step)
mean = x0 * torch.exp(-0.5 * cum_beta / (self.sigma**2))
variance = (self.sigma**2) * (1 - torch.exp(-cum_beta / (self.sigma**2)))
z = torch.randn(x0.shape, dtype=x0.dtype, device=x0.device, requires_grad=False)
xt = mean + z * torch.sqrt(variance) * self.noise_factor
return xt, z
@torch.no_grad()
def cal_dxt(
self, xt, condition_embedding, x_mask, reference_embedding, diffusion_step, h
):
time_step = diffusion_step.unsqueeze(-1).unsqueeze(-1)
cum_beta = self.get_cum_beta(time_step=time_step)
beta_t = self.get_beta_t(time_step=time_step)
x0_pred = self.diff_estimator(
xt, condition_embedding, x_mask, reference_embedding, diffusion_step
)
mean_pred = x0_pred * torch.exp(-0.5 * cum_beta / (self.sigma**2))
noise_pred = xt - mean_pred
variance = (self.sigma**2) * (1.0 - torch.exp(-cum_beta / (self.sigma**2)))
logp = -noise_pred / (variance + 1e-8)
dxt = -0.5 * h * beta_t * (logp + xt / (self.sigma**2))
return dxt
@torch.no_grad()
def reverse_diffusion(
self, z, condition_embedding, x_mask, reference_embedding, n_timesteps
):
h = 1.0 / max(n_timesteps, 1)
xt = z
for i in range(n_timesteps):
t = (1.0 - (i + 0.5) * h) * torch.ones(
z.shape[0], dtype=z.dtype, device=z.device
)
dxt = self.cal_dxt(
xt,
condition_embedding,
x_mask,
reference_embedding,
diffusion_step=t,
h=h,
)
xt_ = xt - dxt
if self.cfg.ode_solve_method == "midpoint":
x_mid = 0.5 * (xt_ + xt)
dxt = self.cal_dxt(
x_mid,
condition_embedding,
x_mask,
reference_embedding,
diffusion_step=t + 0.5 * h,
h=h,
)
xt = xt - dxt
elif self.cfg.ode_solve_method == "euler":
xt = xt_
return xt
@torch.no_grad()
def reverse_diffusion_from_t(
self, z, condition_embedding, x_mask, reference_embedding, n_timesteps, t_start
):
h = t_start / max(n_timesteps, 1)
xt = z
for i in range(n_timesteps):
t = (t_start - (i + 0.5) * h) * torch.ones(
z.shape[0], dtype=z.dtype, device=z.device
)
dxt = self.cal_dxt(
xt,
x_mask,
condition_embedding,
x_mask,
reference_embedding,
diffusion_step=t,
h=h,
)
xt_ = xt - dxt
if self.cfg.ode_solve_method == "midpoint":
x_mid = 0.5 * (xt_ + xt)
dxt = self.cal_dxt(
x_mid,
condition_embedding,
x_mask,
reference_embedding,
diffusion_step=t + 0.5 * h,
h=h,
)
xt = xt - dxt
elif self.cfg.ode_solve_method == "euler":
xt = xt_
return xt
class SinusoidalPosEmb(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, x):
device = x.device
half_dim = self.dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
emb = x[:, None] * emb[None, :]
emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
return emb
class Linear2(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
self.linear_1 = nn.Linear(dim, dim * 2)
self.linear_2 = nn.Linear(dim * 2, dim)
self.linear_1.weight.data.normal_(0.0, 0.02)
self.linear_2.weight.data.normal_(0.0, 0.02)
def forward(self, x):
x = self.linear_1(x)
x = F.silu(x)
x = self.linear_2(x)
return x
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.silu(x)
x = F.dropout(x, self.encoder_dropout, training=self.training)
x = self.ffn_2(x)
return x
class TransformerFFNLayerOld(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.Linear(self.encoder_hidden, self.conv_filter_size)
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 = self.ffn_1(x)
x = F.silu(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,
use_skip_connection,
use_new_ffn,
add_diff_step,
):
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
self.use_skip_connection = use_skip_connection
self.use_new_ffn = use_new_ffn
self.add_diff_step = add_diff_step
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
)
if self.use_new_ffn:
self.ffn = TransformerFFNLayer(
self.encoder_hidden,
self.conv_filter_size,
self.conv_kernel_size,
self.encoder_dropout,
)
else:
self.ffn = TransformerFFNLayerOld(
self.encoder_hidden,
self.conv_filter_size,
self.conv_kernel_size,
self.encoder_dropout,
)
if self.use_skip_connection:
self.skip_linear = nn.Linear(self.encoder_hidden * 2, self.encoder_hidden)
self.skip_linear.weight.data.normal_(0.0, 0.02)
self.skip_layernorm = nn.LayerNorm(self.encoder_hidden)
if self.add_diff_step:
self.diff_step_emb = SinusoidalPosEmb(dim=self.encoder_hidden)
# self.diff_step_projection = nn.linear(self.encoder_hidden, self.encoder_hidden)
# self.encoder_hidden.weight.data.normal_(0.0, 0.02)
self.diff_step_projection = Linear2(self.encoder_hidden)
def forward(
self, x, key_padding_mask, conditon=None, skip_res=None, diffusion_step=None
):
# x: (B, T, d); key_padding_mask: (B, T), mask is 0; condition: (B, T, d); skip_res: (B, T, d); diffusion_step: (B,)
if self.use_skip_connection and skip_res != None:
x = torch.cat([x, skip_res], dim=-1) # (B, T, 2*d)
x = self.skip_linear(x)
x = self.skip_layernorm(x)
if self.add_diff_step and diffusion_step != None:
diff_step_embedding = self.diff_step_emb(diffusion_step)
diff_step_embedding = self.diff_step_projection(diff_step_embedding)
x = x + diff_step_embedding.unsqueeze(1)
residual = x
# pre norm
if self.use_cln:
x = self.ln_1(x, conditon)
else:
x = self.ln_1(x)
# self attention
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
# pre norm
residual = x
if self.use_cln:
x = self.ln_2(x, conditon)
else:
x = self.ln_2(x)
# ffn
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,
use_skip_connection=None,
use_new_ffn=None,
add_diff_step=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
self.use_skip_connection = (
use_skip_connection
if use_skip_connection is not None
else cfg.use_skip_connection
)
self.add_diff_step = (
add_diff_step if add_diff_step is not None else cfg.add_diff_step
)
self.use_new_ffn = use_new_ffn if use_new_ffn is not None else cfg.use_new_ffn
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([])
if self.use_skip_connection:
self.layers.extend(
[
TransformerEncoderLayer(
self.encoder_hidden,
self.encoder_head,
self.conv_filter_size,
self.conv_kernel_size,
self.encoder_dropout,
self.use_cln,
use_skip_connection=False,
use_new_ffn=self.use_new_ffn,
add_diff_step=self.add_diff_step,
)
for i in range(
(self.encoder_layer + 1) // 2
) # for example: 12 -> 6; 13 -> 7
]
)
self.layers.extend(
[
TransformerEncoderLayer(
self.encoder_hidden,
self.encoder_head,
self.conv_filter_size,
self.conv_kernel_size,
self.encoder_dropout,
self.use_cln,
use_skip_connection=True,
use_new_ffn=self.use_new_ffn,
add_diff_step=self.add_diff_step,
)
for i in range(
self.encoder_layer - (self.encoder_layer + 1) // 2
) # 12 -> 6; 13 -> 6
]
)
else:
self.layers.extend(
[
TransformerEncoderLayer(
self.encoder_hidden,
self.encoder_head,
self.conv_filter_size,
self.conv_kernel_size,
self.encoder_dropout,
self.use_cln,
use_new_ffn=self.use_new_ffn,
add_diff_step=self.add_diff_step,
use_skip_connection=False,
)
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)
if self.add_diff_step:
self.diff_step_emb = SinusoidalPosEmb(dim=self.encoder_hidden)
# self.diff_step_projection = nn.linear(self.encoder_hidden, self.encoder_hidden)
# self.encoder_hidden.weight.data.normal_(0.0, 0.02)
self.diff_step_projection = Linear2(self.encoder_hidden)
def forward(self, x, key_padding_mask, condition=None, diffusion_step=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)
if self.add_diff_step and diffusion_step != None:
diff_step_embedding = self.diff_step_emb(diffusion_step)
diff_step_embedding = self.diff_step_projection(diff_step_embedding)
x = x + diff_step_embedding.unsqueeze(1)
if self.use_skip_connection:
skip_res_list = []
# down
for layer in self.layers[: self.encoder_layer // 2]:
x = layer(x, key_padding_mask, condition)
res = x
skip_res_list.append(res)
# middle
for layer in self.layers[
self.encoder_layer // 2 : (self.encoder_layer + 1) // 2
]:
x = layer(x, key_padding_mask, condition)
# up
for layer in self.layers[(self.encoder_layer + 1) // 2 :]:
skip_res = skip_res_list.pop()
x = layer(x, key_padding_mask, condition, skip_res)
else:
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 DiffTransformer(nn.Module):
def __init__(
self,
encoder_layer=None,
encoder_hidden=None,
encoder_head=None,
conv_filter_size=None,
conv_kernel_size=None,
encoder_dropout=None,
use_cln=None,
use_skip_connection=None,
use_new_ffn=None,
add_diff_step=None,
cat_diff_step=None,
in_dim=None,
out_dim=None,
cond_dim=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
self.use_skip_connection = (
use_skip_connection
if use_skip_connection is not None
else cfg.use_skip_connection
)
self.use_new_ffn = use_new_ffn if use_new_ffn is not None else cfg.use_new_ffn
self.add_diff_step = (
add_diff_step if add_diff_step is not None else cfg.add_diff_step
)
self.cat_diff_step = (
cat_diff_step if cat_diff_step is not None else cfg.cat_diff_step
)
self.in_dim = in_dim if in_dim is not None else cfg.in_dim
self.out_dim = out_dim if out_dim is not None else cfg.out_dim
self.cond_dim = cond_dim if cond_dim is not None else cfg.cond_dim
if self.in_dim != self.encoder_hidden:
self.in_linear = nn.Linear(self.in_dim, self.encoder_hidden)
self.in_linear.weight.data.normal_(0.0, 0.02)
else:
self.in_dim = None
if self.out_dim != self.encoder_hidden:
self.out_linear = nn.Linear(self.encoder_hidden, self.out_dim)
self.out_linear.weight.data.normal_(0.0, 0.02)
else:
self.out_dim = None
assert not ((self.cat_diff_step == True) and (self.add_diff_step == True))
self.transformer_encoder = TransformerEncoder(
encoder_layer=self.encoder_layer,
encoder_hidden=self.encoder_hidden,
encoder_head=self.encoder_head,
conv_kernel_size=self.conv_kernel_size,
conv_filter_size=self.conv_filter_size,
encoder_dropout=self.encoder_dropout,
use_cln=self.use_cln,
use_skip_connection=self.use_skip_connection,
use_new_ffn=self.use_new_ffn,
add_diff_step=self.add_diff_step,
)
self.cond_project = nn.Linear(self.cond_dim, self.encoder_hidden)
self.cond_project.weight.data.normal_(0.0, 0.02)
self.cat_linear = nn.Linear(self.encoder_hidden * 2, self.encoder_hidden)
self.cat_linear.weight.data.normal_(0.0, 0.02)
if self.cat_diff_step:
self.diff_step_emb = SinusoidalPosEmb(dim=self.encoder_hidden)
self.diff_step_projection = Linear2(self.encoder_hidden)
def forward(
self,
x,
condition_embedding,
key_padding_mask=None,
reference_embedding=None,
diffusion_step=None,
):
# x: shape is (B, T, d_x)
# key_padding_mask: shape is (B, T), mask is 0
# condition_embedding: from condition adapter, shape is (B, T, d_c)
# reference_embedding: from reference encoder, shape is (B, N, d_r), or (B, 1, d_r), or (B, d_r)
if self.in_linear != None:
x = self.in_linear(x)
condition_embedding = self.cond_project(condition_embedding)
x = torch.cat([x, condition_embedding], dim=-1)
x = self.cat_linear(x)
if self.cat_diff_step and diffusion_step != None:
diff_step_embedding = self.diff_step_emb(diffusion_step)
diff_step_embedding = self.diff_step_projection(
diff_step_embedding
).unsqueeze(
1
) # (B, 1, d)
x = torch.cat([diff_step_embedding, x], dim=1)
if key_padding_mask != None:
key_padding_mask = torch.cat(
[
key_padding_mask,
torch.ones(key_padding_mask.shape[0], 1).to(
key_padding_mask.device
),
],
dim=1,
)
x = self.transformer_encoder(
x,
key_padding_mask=key_padding_mask,
condition=reference_embedding,
diffusion_step=diffusion_step,
)
if self.cat_diff_step and diffusion_step != None:
x = x[:, 1:, :]
if self.out_linear != None:
x = self.out_linear(x)
return x
class ReferenceEncoder(nn.Module):
def __init__(
self,
encoder_layer=None,
encoder_hidden=None,
encoder_head=None,
conv_filter_size=None,
conv_kernel_size=None,
encoder_dropout=None,
use_skip_connection=None,
use_new_ffn=None,
ref_in_dim=None,
ref_out_dim=None,
use_query_emb=None,
num_query_emb=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_skip_connection = (
use_skip_connection
if use_skip_connection is not None
else cfg.use_skip_connection
)
self.use_new_ffn = use_new_ffn if use_new_ffn is not None else cfg.use_new_ffn
self.in_dim = ref_in_dim if ref_in_dim is not None else cfg.ref_in_dim
self.out_dim = ref_out_dim if ref_out_dim is not None else cfg.ref_out_dim
self.use_query_emb = (
use_query_emb if use_query_emb is not None else cfg.use_query_emb
)
self.num_query_emb = (
num_query_emb if num_query_emb is not None else cfg.num_query_emb
)
if self.in_dim != self.encoder_hidden:
self.in_linear = nn.Linear(self.in_dim, self.encoder_hidden)
self.in_linear.weight.data.normal_(0.0, 0.02)
else:
self.in_dim = None
if self.out_dim != self.encoder_hidden:
self.out_linear = nn.Linear(self.encoder_hidden, self.out_dim)
self.out_linear.weight.data.normal_(0.0, 0.02)
else:
self.out_linear = None
self.transformer_encoder = TransformerEncoder(
encoder_layer=self.encoder_layer,
encoder_hidden=self.encoder_hidden,
encoder_head=self.encoder_head,
conv_kernel_size=self.conv_kernel_size,
conv_filter_size=self.conv_filter_size,
encoder_dropout=self.encoder_dropout,
use_new_ffn=self.use_new_ffn,
use_cln=False,
use_skip_connection=False,
add_diff_step=False,
)
if self.use_query_emb:
# 32 x 512
self.query_embs = nn.Embedding(self.num_query_emb, self.encoder_hidden)
self.query_attn = nn.MultiheadAttention(
self.encoder_hidden, self.encoder_hidden // 64, batch_first=True
)
def forward(self, x_ref, key_padding_mask=None):
# x_ref: (B, T, d_ref)
# key_padding_mask: (B, T)
# return speaker embedding: x_spk
# if self.use_query_embs: shape is (B, N_query, d_out)
# else: shape is (B, T, d_out)
if self.in_linear != None:
# print('x_ref:',x_ref.shape)
x = self.in_linear(x_ref)
x = self.transformer_encoder(
x, key_padding_mask=key_padding_mask, condition=None, diffusion_step=None
) # B, T, d_out
if self.use_query_emb:
spk_query_emb = self.query_embs(
torch.arange(self.num_query_emb).to(x.device)
).repeat(x.shape[0], 1, 1)
# k/v b x t x d
# q b x n x d
spk_embs, _ = self.query_attn(
query=spk_query_emb,
key=x,
value=x,
key_padding_mask=(
~(key_padding_mask.bool()) if key_padding_mask is not None else None
),
) # B, N_query, d_out
if self.out_linear != None:
spk_embs = self.out_linear(spk_embs)
else:
spk_query_emb = None
# B x n x d
# b x t x d
return spk_embs, 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 FiLM(nn.Module):
def __init__(self, in_dim, cond_dim):
super().__init__()
self.gain = Linear(cond_dim, in_dim)
self.bias = Linear(cond_dim, in_dim)
nn.init.xavier_uniform_(self.gain.weight)
nn.init.constant_(self.gain.bias, 1)
nn.init.xavier_uniform_(self.bias.weight)
nn.init.constant_(self.bias.bias, 0)
def forward(self, x, condition):
gain = self.gain(condition)
bias = self.bias(condition)
if gain.dim() == 2:
gain = gain.unsqueeze(-1)
if bias.dim() == 2:
bias = bias.unsqueeze(-1)
return x * gain + bias
class Mish(nn.Module):
def forward(self, x):
return x * torch.tanh(F.softplus(x))
def Conv1d(*args, **kwargs):
layer = nn.Conv1d(*args, **kwargs)
layer.weight.data.normal_(0.0, 0.02)
return layer
def Linear(*args, **kwargs):
layer = nn.Linear(*args, **kwargs)
layer.weight.data.normal_(0.0, 0.02)
return layer
class ResidualBlock(nn.Module):
def __init__(self, hidden_dim, attn_head, dilation, drop_out, has_cattn=False):
super().__init__()
self.hidden_dim = hidden_dim
self.dilation = dilation
self.has_cattn = has_cattn
self.attn_head = attn_head
self.drop_out = drop_out
self.dilated_conv = Conv1d(
hidden_dim, 2 * hidden_dim, 3, padding=dilation, dilation=dilation
)
self.diffusion_proj = Linear(hidden_dim, hidden_dim)
self.cond_proj = Conv1d(hidden_dim, hidden_dim * 2, 1)
self.out_proj = Conv1d(hidden_dim, hidden_dim * 2, 1)
if self.has_cattn:
self.attn = nn.MultiheadAttention(
hidden_dim, attn_head, 0.1, batch_first=True
)
self.film = FiLM(hidden_dim * 2, hidden_dim)
self.ln = nn.LayerNorm(hidden_dim)
self.dropout = nn.Dropout(self.drop_out)
def forward(self, x, x_mask, cond, diffusion_step, spk_query_emb):
diffusion_step = self.diffusion_proj(diffusion_step).unsqueeze(-1) # (B, d, 1)
cond = self.cond_proj(cond) # (B, 2*d, T)
y = x + diffusion_step
if x_mask != None:
y = y * x_mask.to(y.dtype)[:, None, :] # (B, 2*d, T)
if self.has_cattn:
y_ = y.transpose(1, 2)
y_ = self.ln(y_)
y_, _ = self.attn(y_, spk_query_emb, spk_query_emb) # (B, T, d)
y = self.dilated_conv(y) + cond # (B, 2*d, T)
if self.has_cattn:
y = self.film(y.transpose(1, 2), y_) # (B, T, 2*d)
y = y.transpose(1, 2) # (B, 2*d, T)
gate, filter_ = torch.chunk(y, 2, dim=1)
y = torch.sigmoid(gate) * torch.tanh(filter_)
y = self.out_proj(y)
residual, skip = torch.chunk(y, 2, dim=1)
if x_mask != None:
residual = residual * x_mask.to(y.dtype)[:, None, :]
skip = skip * x_mask.to(y.dtype)[:, None, :]
return (x + residual) / math.sqrt(2.0), skip
class DiffWaveNet(nn.Module):
def __init__(
self,
cfg=None,
):
super().__init__()
self.cfg = cfg
self.in_dim = cfg.input_size
self.hidden_dim = cfg.hidden_size
self.out_dim = cfg.out_size
self.num_layers = cfg.num_layers
self.cross_attn_per_layer = cfg.cross_attn_per_layer
self.dilation_cycle = cfg.dilation_cycle
self.attn_head = cfg.attn_head
self.drop_out = cfg.drop_out
self.in_proj = Conv1d(self.in_dim, self.hidden_dim, 1)
self.diffusion_embedding = SinusoidalPosEmb(self.hidden_dim)
self.mlp = nn.Sequential(
Linear(self.hidden_dim, self.hidden_dim * 4),
Mish(),
Linear(self.hidden_dim * 4, self.hidden_dim),
)
self.cond_ln = nn.LayerNorm(self.hidden_dim)
self.layers = nn.ModuleList(
[
ResidualBlock(
self.hidden_dim,
self.attn_head,
2 ** (i % self.dilation_cycle),
self.drop_out,
has_cattn=(i % self.cross_attn_per_layer == 0),
)
for i in range(self.num_layers)
]
)
self.skip_proj = Conv1d(self.hidden_dim, self.hidden_dim, 1)
self.out_proj = Conv1d(self.hidden_dim, self.out_dim, 1)
nn.init.zeros_(self.out_proj.weight)
def forward(
self,
x,
condition_embedding,
key_padding_mask=None,
reference_embedding=None,
diffusion_step=None,
):
x = x.transpose(1, 2) # (B, T, d) -> (B, d, T)
x_mask = key_padding_mask
cond = condition_embedding
spk_query_emb = reference_embedding
diffusion_step = diffusion_step
cond = self.cond_ln(cond)
cond_input = cond.transpose(1, 2)
x_input = self.in_proj(x)
x_input = F.relu(x_input)
diffusion_step = self.diffusion_embedding(diffusion_step).to(x.dtype)
diffusion_step = self.mlp(diffusion_step)
skip = []
for _, layer in enumerate(self.layers):
x_input, skip_connection = layer(
x_input, x_mask, cond_input, diffusion_step, spk_query_emb
)
skip.append(skip_connection)
x_input = torch.sum(torch.stack(skip), dim=0) / math.sqrt(self.num_layers)
x_out = self.skip_proj(x_input)
x_out = F.relu(x_out)
x_out = self.out_proj(x_out) # (B, 80, T)
x_out = x_out.transpose(1, 2)
return x_out
def override_config(base_config, new_config):
"""Update new configurations in the original dict with the new dict
Args:
base_config (dict): original dict to be overridden
new_config (dict): dict with new configurations
Returns:
dict: updated configuration dict
"""
for k, v in new_config.items():
if type(v) == dict:
if k not in base_config.keys():
base_config[k] = {}
base_config[k] = override_config(base_config[k], v)
else:
base_config[k] = v
return base_config
def get_lowercase_keys_config(cfg):
"""Change all keys in cfg to lower case
Args:
cfg (dict): dictionary that stores configurations
Returns:
dict: dictionary that stores configurations
"""
updated_cfg = dict()
for k, v in cfg.items():
if type(v) == dict:
v = get_lowercase_keys_config(v)
updated_cfg[k.lower()] = v
return updated_cfg
def save_config(save_path, cfg):
"""Save configurations into a json file
Args:
save_path (str): path to save configurations
cfg (dict): dictionary that stores configurations
"""
with open(save_path, "w") as f:
json5.dump(
cfg, f, ensure_ascii=False, indent=4, quote_keys=True, sort_keys=True
)
class JsonHParams:
def __init__(self, **kwargs):
for k, v in kwargs.items():
if type(v) == dict:
v = JsonHParams(**v)
self[k] = v
def keys(self):
return self.__dict__.keys()
def items(self):
return self.__dict__.items()
def values(self):
return self.__dict__.values()
def __len__(self):
return len(self.__dict__)
def __getitem__(self, key):
return getattr(self, key)
def __setitem__(self, key, value):
return setattr(self, key, value)
def __contains__(self, key):
return key in self.__dict__
def __repr__(self):
return self.__dict__.__repr__()
class Noro_VCmodel(nn.Module):
def __init__(self, cfg, use_ref_noise=False):
super().__init__()
self.cfg = cfg
self.use_ref_noise = use_ref_noise
self.reference_encoder = ReferenceEncoder(cfg=cfg.reference_encoder)
if cfg.diffusion.diff_model_type == "WaveNet":
self.diffusion = Diffusion(
cfg=cfg.diffusion,
diff_model=DiffWaveNet(cfg=cfg.diffusion.diff_wavenet),
)
else:
raise NotImplementedError()
pitch_dim = 1
self.content_f0_enc = nn.Sequential(
nn.LayerNorm(
cfg.vc_feature.content_feature_dim + pitch_dim
), # 768 (for mhubert) + 1 (for f0)
Rearrange("b t d -> b d t"),
nn.Conv1d(
cfg.vc_feature.content_feature_dim + pitch_dim,
cfg.vc_feature.hidden_dim,
kernel_size=3,
padding=1,
),
Rearrange("b d t -> b t d"),
)
self.reset_parameters()
def forward(
self,
x=None,
content_feature=None,
pitch=None,
x_ref=None,
x_mask=None,
x_ref_mask=None,
noisy_x_ref=None,
):
noisy_reference_embedding = None
noisy_condition_embedding = None
reference_embedding, encoded_x = self.reference_encoder(
x_ref=x_ref, key_padding_mask=x_ref_mask
)
# content_feature: B x T x D
# pitch: B x T x 1
# B x t x D+1
# 2B x T
condition_embedding = torch.cat([content_feature, pitch[:, :, None]], dim=-1)
condition_embedding = self.content_f0_enc(condition_embedding)
# 2B x T x D
if self.use_ref_noise:
# noisy_reference
noisy_reference_embedding, _ = self.reference_encoder(
x_ref=noisy_x_ref, key_padding_mask=x_ref_mask
)
combined_reference_embedding = (
noisy_reference_embedding + reference_embedding
) / 2
else:
combined_reference_embedding = reference_embedding
combined_condition_embedding = condition_embedding
diff_out = self.diffusion(
x=x,
condition_embedding=combined_condition_embedding,
x_mask=x_mask,
reference_embedding=combined_reference_embedding,
)
return (
diff_out,
(reference_embedding, noisy_reference_embedding),
(condition_embedding, noisy_condition_embedding),
)
@torch.no_grad()
def inference(
self,
content_feature=None,
pitch=None,
x_ref=None,
x_ref_mask=None,
inference_steps=1000,
sigma=1.2,
):
reference_embedding, _ = self.reference_encoder(
x_ref=x_ref, key_padding_mask=x_ref_mask
)
condition_embedding = torch.cat([content_feature, pitch[:, :, None]], dim=-1)
condition_embedding = self.content_f0_enc(condition_embedding)
bsz, l, _ = condition_embedding.shape
if self.cfg.diffusion.diff_model_type == "WaveNet":
z = (
torch.randn(bsz, l, self.cfg.diffusion.diff_wavenet.input_size).to(
condition_embedding.device
)
/ sigma
)
x0 = self.diffusion.reverse_diffusion(
z=z,
condition_embedding=condition_embedding,
x_mask=None,
reference_embedding=reference_embedding,
n_timesteps=inference_steps,
)
return x0
def reset_parameters(self):
def _reset_parameters(m):
if isinstance(m, nn.MultiheadAttention):
if m._qkv_same_embed_dim:
nn.init.normal_(m.in_proj_weight, std=0.02)
else:
nn.init.normal_(m.q_proj_weight, std=0.02)
nn.init.normal_(m.k_proj_weight, std=0.02)
nn.init.normal_(m.v_proj_weight, std=0.02)
if m.in_proj_bias is not None:
nn.init.constant_(m.in_proj_bias, 0.0)
nn.init.constant_(m.out_proj.bias, 0.0)
if m.bias_k is not None:
nn.init.xavier_normal_(m.bias_k)
if m.bias_v is not None:
nn.init.xavier_normal_(m.bias_v)
elif (
isinstance(m, nn.Conv1d)
or isinstance(m, nn.ConvTranspose1d)
or isinstance(m, nn.Conv2d)
or isinstance(m, nn.ConvTranspose2d)
):
m.weight.data.normal_(0.0, 0.02)
elif isinstance(m, nn.Linear):
m.weight.data.normal_(mean=0.0, std=0.02)
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.Embedding):
m.weight.data.normal_(mean=0.0, std=0.02)
if m.padding_idx is not None:
m.weight.data[m.padding_idx].zero_()
self.apply(_reset_parameters)
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