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11b4f27 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 | from module.wavenet import SinusoidalPosEmb, WaveNet
from text import symbols
from transformer.embedding import SinePositionalEmbedding
from transformer.transformer import LayerNorm, TransformerEncoder, TransformerEncoderLayer
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from module.models import TextEncoder, PosteriorEncoder, FramePriorNet, Generator, ProsodyEncoder, ResidualCouplingBlock
from module import commons
from module import modules
import math
import random
from tqdm import tqdm
class NARPredictor(nn.Module):
def __init__(self, codebook_size=128, gin_channels=0):
super().__init__()
norm_first = False
self.encoder = TransformerEncoder(
TransformerEncoderLayer(
d_model=512,
nhead=16,
dim_feedforward=2048,
dropout=0.1,
batch_first=True,
norm_first=False, ),
num_layers=24,
norm=LayerNorm(512) if norm_first else None, )
self.num_head = 16
self.position = SinePositionalEmbedding(
512, dropout=0.1, scale=False, alpha=True)
self.bert_proj = nn.Linear(1024, 512)
self.x_proj = nn.Embedding(1024, 512)
self.g_emb = nn.Linear(256, 512)
self.proj = nn.Linear(512, codebook_size, bias=False)
self.quantize_embedding = nn.Embedding(codebook_size + 1, 512)
self.n_codes = codebook_size
def forward_encoder(self, x, mask=None):
if mask is not None:
mask = ~mask.squeeze(1).bool()
x = self.position(x)
x, _ = self.encoder(
(x, None), src_key_padding_mask=mask)
audio_output = self.proj(x)
return audio_output
def forward(self, x, x_mask, bert, spk_emb, tgt_code):
g = self.g_emb(spk_emb).unsqueeze(1)
bert = self.bert_proj(bert.transpose(1, 2))
x = self.x_proj(x)
x = x + bert + g
# make mask
quantization = tgt_code.clone()
mask_ratio = random.uniform(0, 1)
mask = torch.bernoulli(mask_ratio * torch.ones_like(quantization)).bool()
quantization[mask] = self.n_codes
quantization_emb = self.quantize_embedding(quantization)
inp = x + quantization_emb
audio_output = self.forward_encoder(inp, mask=x_mask)
tgt_mask = x_mask.squeeze(1).bool()
audio_output = audio_output[tgt_mask]
tgt_code = tgt_code[tgt_mask].view(-1)
predict_loss = F.cross_entropy(audio_output, tgt_code, reduction='mean')
acc = (audio_output.argmax(-1) == tgt_code).float().mean() * 100
print('prosody acc', acc.item())
return predict_loss
@torch.inference_mode()
@torch.no_grad()
def infer(self, x, x_mask, bert, spk_emb, step=5, sched_mode="square", temp=30, randomize="None", r_temp=30):
g = self.g_emb(spk_emb).unsqueeze(1)
bert = self.bert_proj(bert.transpose(1, 2))
x = self.x_proj(x)
x = x + bert + g
nb_sample = x.size(0)
tgt_length = x.size(1)
code = torch.full((nb_sample, tgt_length), self.n_codes).long().to(x.device)
mask = torch.ones((nb_sample, tgt_length)).to(x.device)
scheduler = self.adap_sche(step, mode=sched_mode, tgt_length=tgt_length)
for indice, t in enumerate(scheduler):
# print('step:', indice, '\nmask:', mask, '\nt:', t, '\ncode', code, '\n\n')
if mask.sum() < t: # Cannot predict more token than 16*16 or 32*32
t = int(mask.sum().item())
print(f"Warning: cannot predict {t} tokens, only {mask.sum().item()} tokens left")
if mask.sum() == 0: # Break if code is fully predicted
break
quantization_emb = self.quantize_embedding(code.clone())
inp = x + quantization_emb
logit = self.forward_encoder(inp)
prob = torch.softmax(logit * temp, -1)
distri = torch.distributions.Categorical(probs=prob)
pred_code = distri.sample()
conf = torch.gather(prob, 2, pred_code.view(nb_sample, tgt_length, 1))
if randomize == "linear": # add gumbel noise decreasing over the sampling process
ratio = (indice / (len(scheduler) - 1))
rand = r_temp * np.random.gumbel(size=(nb_sample, tgt_length)) * (1 - ratio)
conf = torch.log(conf.squeeze()) + torch.from_numpy(rand).to(x.device)
elif randomize == "warm_up": # chose random sample for the 2 first steps
conf = torch.rand_like(conf) if indice < 2 else conf
elif randomize == "random": # chose random prediction at each step
conf = torch.rand_like(conf)
# do not predict on already predicted tokens
conf[~mask.bool()] = -math.inf
# chose the predicted token with the highest confidence
tresh_conf, indice_mask = torch.topk(conf.view(nb_sample, -1), k=t, dim=-1)
tresh_conf = tresh_conf[:, -1]
# replace the chosen tokens
conf = (conf >= tresh_conf.unsqueeze(-1)).view(nb_sample, tgt_length)
f_mask = (mask.float() * conf.float()).bool()
code[f_mask] = pred_code[f_mask]
# update the mask
for i_mask, ind_mask in enumerate(indice_mask):
mask[i_mask, ind_mask] = 0
return code
def adap_sche(self, step, mode="arccos", leave=False, tgt_length=None):
""" Create a sampling scheduler
:param
step -> int: number of prediction during inference
mode -> str: the rate of value to unmask
leave -> bool: tqdm arg on either to keep the bar or not
:return
scheduler -> torch.LongTensor(): the list of token to predict at each step
"""
r = torch.linspace(1, 0, step)
if mode == "root": # root scheduler
val_to_mask = 1 - (r ** .5)
elif mode == "linear": # linear scheduler
val_to_mask = 1 - r
elif mode == "square": # square scheduler
val_to_mask = 1 - (r ** 2)
elif mode == "cosine": # cosine scheduler
val_to_mask = torch.cos(r * math.pi * 0.5)
elif mode == "arccos": # arc cosine scheduler
val_to_mask = torch.arccos(r) / (math.pi * 0.5)
else:
return
# fill the scheduler by the ratio of tokens to predict at each step
sche = (val_to_mask / val_to_mask.sum()) * tgt_length
sche = sche.round()
sche[sche == 0] = 1 # add 1 to predict a least 1 token / step
sche[-1] += tgt_length - sche.sum() # need to sum up nb of code
return tqdm(sche.int(), leave=leave)
class SynthesizerTrn(nn.Module):
"""
Synthesizer for Training
"""
def __init__(self,
spec_channels,
segment_size,
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=0,
gin_channels=0,
use_sdp=True,
freeze_quantizer=False,
**kwargs):
super().__init__()
self.spec_channels = spec_channels
self.inter_channels = inter_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.resblock = resblock
self.resblock_kernel_sizes = resblock_kernel_sizes
self.resblock_dilation_sizes = resblock_dilation_sizes
self.upsample_rates = upsample_rates
self.upsample_initial_channel = upsample_initial_channel
self.upsample_kernel_sizes = upsample_kernel_sizes
self.segment_size = segment_size
self.n_speakers = n_speakers
gin_channels = hidden_channels
self.gin_channels = gin_channels
self.use_sdp = use_sdp
self.enc_p = TextEncoder(inter_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout)
print('n_param of enc_p', sum(p.numel() for p in self.enc_p.parameters() if p.requires_grad) / 1e6, 'M')
self.frame_prior_net = FramePriorNet(inter_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout)
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, 3, gin_channels=gin_channels
)
self.ref_enc = modules.MelStyleEncoder(spec_channels, style_vector_dim=gin_channels)
vq_dim = 512
self.prosody_encoder = ProsodyEncoder(768, hidden_channels, 5, 1, 6, gin_channels, vq_dim)
self.prosody_predictor = NARPredictor(vq_dim, gin_channels=gin_channels)
def forward(self, x, x_lengths, y, y_lengths, ssl, duration, bert, spk_emb, prosody_predict_only=False):
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(-1)), 1).to(y.dtype)
text_long = x.clone()
g = self.ref_enc(y * y_mask, y_mask)
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(-1)), 1).to(y.dtype)
attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
attn = commons.generate_path(duration.unsqueeze(1), attn_mask)
v_mask = (x > 0).float()
z_q, quantize_loss, indices, dur_loss, pred_dur = self.prosody_encoder(ssl, x_mask, attn, duration, v_mask)
prosody_predict_loss = self.prosody_predictor(x, x_mask, bert, spk_emb, indices)
return prosody_predict_loss, indices
def reconstruct(self, x, x_lengths, y, y_lengths, ssl, duration, bert, spk_emb, noise_scale=.4):
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y.size(-1)), 1).to(y.dtype)
g = self.ref_enc(y * y_mask, y_mask)
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(-1)), 1).to(y.dtype)
# attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
# attn = commons.generate_path(duration.unsqueeze(1), attn_mask)
# v_mask = (x > 0).float()
# _, _, pred_prosody_codes, _, _ = self.prosody_encoder(ssl, x_mask, attn, duration,v_mask)
pred_prosody_codes = self.prosody_predictor.infer(x, x_mask, bert, spk_emb)
x = self.enc_p(x, x_mask, bert)
z_q, duration = self.prosody_encoder.decode(pred_prosody_codes, x_mask)
total_duration = torch.sum(duration, dim=1)
y_lengths = total_duration.long()
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y_lengths.max()), 1).float()
attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
attn = commons.generate_path(duration.unsqueeze(1), attn_mask)
x = x + z_q
frame_x = torch.matmul(attn.squeeze(1), x.transpose(1, 2)).transpose(1, 2)
m_p, logs_p = self.frame_prior_net(frame_x, y_mask)
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), g=g)
return o, attn, y_mask, (z, z_p, m_p, logs_p)
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