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from modules.fastspeech.tts_modules import FastspeechDecoder
# from modules.fastspeech.fast_tacotron import DecoderRNN
# from modules.fastspeech.speedy_speech.speedy_speech import ConvBlocks
# from modules.fastspeech.conformer.conformer import ConformerDecoder
import torch
from torch.nn import functional as F
import torch.nn as nn
import math
from utils.hparams import hparams
from .diffusion import Mish
Linear = nn.Linear
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
def Conv1d(*args, **kwargs):
layer = nn.Conv1d(*args, **kwargs)
nn.init.kaiming_normal_(layer.weight)
return layer
class FFT(FastspeechDecoder):
def __init__(self, hidden_size=None, num_layers=None, kernel_size=None, num_heads=None):
super().__init__(hidden_size, num_layers, kernel_size, num_heads=num_heads)
dim = hparams['residual_channels']
self.input_projection = Conv1d(hparams['audio_num_mel_bins'], dim, 1)
self.diffusion_embedding = SinusoidalPosEmb(dim)
self.mlp = nn.Sequential(
nn.Linear(dim, dim * 4),
Mish(),
nn.Linear(dim * 4, dim)
)
self.get_mel_out = Linear(hparams['hidden_size'], 80, bias=True)
self.get_decode_inp = Linear(hparams['hidden_size'] + dim + dim,
hparams['hidden_size']) # hs + dim + 80 -> hs
def forward(self, spec, diffusion_step, cond, padding_mask=None, attn_mask=None, return_hiddens=False):
"""
:param spec: [B, 1, 80, T]
:param diffusion_step: [B, 1]
:param cond: [B, M, T]
:return:
"""
x = spec[:, 0]
x = self.input_projection(x).permute([0, 2, 1]) # [B, T, residual_channel]
diffusion_step = self.diffusion_embedding(diffusion_step)
diffusion_step = self.mlp(diffusion_step) # [B, dim]
cond = cond.permute([0, 2, 1]) # [B, T, M]
seq_len = cond.shape[1] # [T_mel]
time_embed = diffusion_step[:, None, :] # [B, 1, dim]
time_embed = time_embed.repeat([1, seq_len, 1]) # # [B, T, dim]
decoder_inp = torch.cat([x, cond, time_embed], dim=-1) # [B, T, dim + H + dim]
decoder_inp = self.get_decode_inp(decoder_inp) # [B, T, H]
x = decoder_inp
'''
Required x: [B, T, C]
:return: [B, T, C] or [L, B, T, C]
'''
padding_mask = x.abs().sum(-1).eq(0).data if padding_mask is None else padding_mask
nonpadding_mask_TB = 1 - padding_mask.transpose(0, 1).float()[:, :, None] # [T, B, 1]
if self.use_pos_embed:
positions = self.pos_embed_alpha * self.embed_positions(x[..., 0])
x = x + positions
x = F.dropout(x, p=self.dropout, training=self.training)
# B x T x C -> T x B x C
x = x.transpose(0, 1) * nonpadding_mask_TB
hiddens = []
for layer in self.layers:
x = layer(x, encoder_padding_mask=padding_mask, attn_mask=attn_mask) * nonpadding_mask_TB
hiddens.append(x)
if self.use_last_norm:
x = self.layer_norm(x) * nonpadding_mask_TB
if return_hiddens:
x = torch.stack(hiddens, 0) # [L, T, B, C]
x = x.transpose(1, 2) # [L, B, T, C]
else:
x = x.transpose(0, 1) # [B, T, C]
x = self.get_mel_out(x).permute([0, 2, 1]) # [B, 80, T]
return x[:, None, :, :] |