File size: 31,242 Bytes
127d53c |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 |
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Tuple, Union
import librosa
import numpy as np
import torch
from coqpit import Coqpit
from torch import nn
from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm
import TTS.vc.modules.freevc.commons as commons
import TTS.vc.modules.freevc.modules as modules
from TTS.tts.utils.speakers import SpeakerManager
from TTS.utils.io import load_fsspec, save_checkpoint
from TTS.vc.configs.shared_configs import BaseVCConfig
from TTS.vc.models.base_vc import BaseVC
from TTS.vc.modules.freevc.commons import get_padding, init_weights
from TTS.vc.modules.freevc.mel_processing import mel_spectrogram_torch
from TTS.vc.modules.freevc.speaker_encoder.speaker_encoder import SpeakerEncoder as SpeakerEncoderEx
from TTS.vc.modules.freevc.wavlm import get_wavlm
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 Encoder(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):
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) * 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 l in self.ups:
remove_weight_norm(l)
for l in self.resblocks:
l.remove_weight_norm()
class DiscriminatorP(torch.nn.Module):
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
super(DiscriminatorP, self).__init__()
self.period = period
self.use_spectral_norm = use_spectral_norm
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
self.convs = nn.ModuleList(
[
norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
]
)
self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
def forward(self, x):
fmap = []
# 1d to 2d
b, c, t = x.shape
if t % self.period != 0: # pad first
n_pad = self.period - (t % self.period)
x = F.pad(x, (0, n_pad), "reflect")
t = t + n_pad
x = x.view(b, c, t // self.period, self.period)
for l in self.convs:
x = l(x)
x = F.leaky_relu(x, modules.LRELU_SLOPE)
fmap.append(x)
x = self.conv_post(x)
fmap.append(x)
x = torch.flatten(x, 1, -1)
return x, fmap
class DiscriminatorS(torch.nn.Module):
def __init__(self, use_spectral_norm=False):
super(DiscriminatorS, self).__init__()
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
self.convs = nn.ModuleList(
[
norm_f(Conv1d(1, 16, 15, 1, padding=7)),
norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
]
)
self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
def forward(self, x):
fmap = []
for l in self.convs:
x = l(x)
x = F.leaky_relu(x, modules.LRELU_SLOPE)
fmap.append(x)
x = self.conv_post(x)
fmap.append(x)
x = torch.flatten(x, 1, -1)
return x, fmap
class MultiPeriodDiscriminator(torch.nn.Module):
def __init__(self, use_spectral_norm=False):
super(MultiPeriodDiscriminator, self).__init__()
periods = [2, 3, 5, 7, 11]
discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
self.discriminators = nn.ModuleList(discs)
def forward(self, y, y_hat):
y_d_rs = []
y_d_gs = []
fmap_rs = []
fmap_gs = []
for i, d in enumerate(self.discriminators):
y_d_r, fmap_r = d(y)
y_d_g, fmap_g = d(y_hat)
y_d_rs.append(y_d_r)
y_d_gs.append(y_d_g)
fmap_rs.append(fmap_r)
fmap_gs.append(fmap_g)
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
class SpeakerEncoder(torch.nn.Module):
def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256):
super(SpeakerEncoder, self).__init__()
self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True)
self.linear = nn.Linear(model_hidden_size, model_embedding_size)
self.relu = nn.ReLU()
def forward(self, mels):
self.lstm.flatten_parameters()
_, (hidden, _) = self.lstm(mels)
embeds_raw = self.relu(self.linear(hidden[-1]))
return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
def compute_partial_slices(self, total_frames, partial_frames, partial_hop):
mel_slices = []
for i in range(0, total_frames - partial_frames, partial_hop):
mel_range = torch.arange(i, i + partial_frames)
mel_slices.append(mel_range)
return mel_slices
def embed_utterance(self, mel, partial_frames=128, partial_hop=64):
mel_len = mel.size(1)
last_mel = mel[:, -partial_frames:]
if mel_len > partial_frames:
mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop)
mels = list(mel[:, s] for s in mel_slices)
mels.append(last_mel)
mels = torch.stack(tuple(mels), 0).squeeze(1)
with torch.no_grad():
partial_embeds = self(mels)
embed = torch.mean(partial_embeds, axis=0).unsqueeze(0)
# embed = embed / torch.linalg.norm(embed, 2)
else:
with torch.no_grad():
embed = self(last_mel)
return embed
@dataclass
class FreeVCAudioConfig(Coqpit):
"""Audio configuration
Args:
max_wav_value (float):
The maximum value of the waveform.
input_sample_rate (int):
The sampling rate of the input waveform.
output_sample_rate (int):
The sampling rate of the output waveform.
filter_length (int):
The length of the filter.
hop_length (int):
The hop length.
win_length (int):
The window length.
n_mel_channels (int):
The number of mel channels.
mel_fmin (float):
The minimum frequency of the mel filterbank.
mel_fmax (Optional[float]):
The maximum frequency of the mel filterbank.
"""
max_wav_value: float = field(default=32768.0)
input_sample_rate: int = field(default=16000)
output_sample_rate: int = field(default=24000)
filter_length: int = field(default=1280)
hop_length: int = field(default=320)
win_length: int = field(default=1280)
n_mel_channels: int = field(default=80)
mel_fmin: float = field(default=0.0)
mel_fmax: Optional[float] = field(default=None)
@dataclass
class FreeVCArgs(Coqpit):
"""FreeVC model arguments
Args:
spec_channels (int):
The number of channels in the spectrogram.
inter_channels (int):
The number of channels in the intermediate layers.
hidden_channels (int):
The number of channels in the hidden layers.
filter_channels (int):
The number of channels in the filter layers.
n_heads (int):
The number of attention heads.
n_layers (int):
The number of layers.
kernel_size (int):
The size of the kernel.
p_dropout (float):
The dropout probability.
resblock (str):
The type of residual block.
resblock_kernel_sizes (List[int]):
The kernel sizes for the residual blocks.
resblock_dilation_sizes (List[List[int]]):
The dilation sizes for the residual blocks.
upsample_rates (List[int]):
The upsample rates.
upsample_initial_channel (int):
The number of channels in the initial upsample layer.
upsample_kernel_sizes (List[int]):
The kernel sizes for the upsample layers.
n_layers_q (int):
The number of layers in the quantization network.
use_spectral_norm (bool):
Whether to use spectral normalization.
gin_channels (int):
The number of channels in the global conditioning vector.
ssl_dim (int):
The dimension of the self-supervised learning embedding.
use_spk (bool):
Whether to use external speaker encoder.
"""
spec_channels: int = field(default=641)
inter_channels: int = field(default=192)
hidden_channels: int = field(default=192)
filter_channels: int = field(default=768)
n_heads: int = field(default=2)
n_layers: int = field(default=6)
kernel_size: int = field(default=3)
p_dropout: float = field(default=0.1)
resblock: str = field(default="1")
resblock_kernel_sizes: List[int] = field(default_factory=lambda: [3, 7, 11])
resblock_dilation_sizes: List[List[int]] = field(default_factory=lambda: [[1, 3, 5], [1, 3, 5], [1, 3, 5]])
upsample_rates: List[int] = field(default_factory=lambda: [10, 8, 2, 2])
upsample_initial_channel: int = field(default=512)
upsample_kernel_sizes: List[int] = field(default_factory=lambda: [16, 16, 4, 4])
n_layers_q: int = field(default=3)
use_spectral_norm: bool = field(default=False)
gin_channels: int = field(default=256)
ssl_dim: int = field(default=1024)
use_spk: bool = field(default=False)
num_spks: int = field(default=0)
segment_size: int = field(default=8960)
class FreeVC(BaseVC):
"""
Papaer::
https://arxiv.org/abs/2210.15418#
Paper Abstract::
Voice conversion (VC) can be achieved by first extracting source content information and target speaker
information, and then reconstructing waveform with these information. However, current approaches normally
either extract dirty content information with speaker information leaked in, or demand a large amount of
annotated data for training. Besides, the quality of reconstructed waveform can be degraded by the
mismatch between conversion model and vocoder. In this paper, we adopt the end-to-end framework of VITS for
high-quality waveform reconstruction, and propose strategies for clean content information extraction without
text annotation. We disentangle content information by imposing an information bottleneck to WavLM features,
and propose the spectrogram-resize based data augmentation to improve the purity of extracted content
information. Experimental results show that the proposed method outperforms the latest VC models trained with
annotated data and has greater robustness.
Original Code::
https://github.com/OlaWod/FreeVC
Examples:
>>> from TTS.vc.configs.freevc_config import FreeVCConfig
>>> from TTS.vc.models.freevc import FreeVC
>>> config = FreeVCConfig()
>>> model = FreeVC(config)
"""
def __init__(self, config: Coqpit, speaker_manager: SpeakerManager = None):
super().__init__(config, None, speaker_manager, None)
self.init_multispeaker(config)
self.spec_channels = self.args.spec_channels
self.inter_channels = self.args.inter_channels
self.hidden_channels = self.args.hidden_channels
self.filter_channels = self.args.filter_channels
self.n_heads = self.args.n_heads
self.n_layers = self.args.n_layers
self.kernel_size = self.args.kernel_size
self.p_dropout = self.args.p_dropout
self.resblock = self.args.resblock
self.resblock_kernel_sizes = self.args.resblock_kernel_sizes
self.resblock_dilation_sizes = self.args.resblock_dilation_sizes
self.upsample_rates = self.args.upsample_rates
self.upsample_initial_channel = self.args.upsample_initial_channel
self.upsample_kernel_sizes = self.args.upsample_kernel_sizes
self.segment_size = self.args.segment_size
self.gin_channels = self.args.gin_channels
self.ssl_dim = self.args.ssl_dim
self.use_spk = self.args.use_spk
self.enc_p = Encoder(self.args.ssl_dim, self.inter_channels, self.hidden_channels, 5, 1, 16)
self.dec = Generator(
self.inter_channels,
self.resblock,
self.resblock_kernel_sizes,
self.resblock_dilation_sizes,
self.upsample_rates,
self.upsample_initial_channel,
self.upsample_kernel_sizes,
gin_channels=self.gin_channels,
)
self.enc_q = Encoder(
self.spec_channels, self.inter_channels, self.hidden_channels, 5, 1, 16, gin_channels=self.gin_channels
)
self.flow = ResidualCouplingBlock(
self.inter_channels, self.hidden_channels, 5, 1, 4, gin_channels=self.gin_channels
)
if not self.use_spk:
self.enc_spk = SpeakerEncoder(model_hidden_size=self.gin_channels, model_embedding_size=self.gin_channels)
else:
self.load_pretrained_speaker_encoder()
self.wavlm = get_wavlm()
@property
def device(self):
return next(self.parameters()).device
def load_pretrained_speaker_encoder(self):
"""Load pretrained speaker encoder model as mentioned in the paper."""
print(" > Loading pretrained speaker encoder model ...")
self.enc_spk_ex = SpeakerEncoderEx(
"https://github.com/coqui-ai/TTS/releases/download/v0.13.0_models/speaker_encoder.pt"
)
def init_multispeaker(self, config: Coqpit):
"""Initialize multi-speaker modules of a model. A model can be trained either with a speaker embedding layer
or with external `d_vectors` computed from a speaker encoder model.
You must provide a `speaker_manager` at initialization to set up the multi-speaker modules.
Args:
config (Coqpit): Model configuration.
data (List, optional): Dataset items to infer number of speakers. Defaults to None.
"""
self.num_spks = self.args.num_spks
if self.speaker_manager:
self.num_spks = self.speaker_manager.num_spks
def forward(
self,
c: torch.Tensor,
spec: torch.Tensor,
g: Optional[torch.Tensor] = None,
mel: Optional[torch.Tensor] = None,
c_lengths: Optional[torch.Tensor] = None,
spec_lengths: Optional[torch.Tensor] = None,
) -> Tuple[
torch.Tensor,
torch.Tensor,
torch.Tensor,
Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor],
]:
"""
Forward pass of the model.
Args:
c: WavLM features. Shape: (batch_size, c_seq_len).
spec: The input spectrogram. Shape: (batch_size, spec_seq_len, spec_dim).
g: The speaker embedding. Shape: (batch_size, spk_emb_dim).
mel: The input mel-spectrogram for the speaker encoder. Shape: (batch_size, mel_seq_len, mel_dim).
c_lengths: The lengths of the WavLM features. Shape: (batch_size,).
spec_lengths: The lengths of the spectrogram. Shape: (batch_size,).
Returns:
o: The output spectrogram. Shape: (batch_size, spec_seq_len, spec_dim).
ids_slice: The slice indices. Shape: (batch_size, num_slices).
spec_mask: The spectrogram mask. Shape: (batch_size, spec_seq_len).
(z, z_p, m_p, logs_p, m_q, logs_q): A tuple of latent variables.
"""
# If c_lengths is None, set it to the length of the last dimension of c
if c_lengths is None:
c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
# If spec_lengths is None, set it to the length of the last dimension of spec
if spec_lengths is None:
spec_lengths = (torch.ones(spec.size(0)) * spec.size(-1)).to(spec.device)
# If use_spk is False, compute g from mel using enc_spk
g = None
if not self.use_spk:
g = self.enc_spk(mel).unsqueeze(-1)
# Compute m_p, logs_p, z, m_q, logs_q, and spec_mask using enc_p and enc_q
_, m_p, logs_p, _ = self.enc_p(c, c_lengths)
z, m_q, logs_q, spec_mask = self.enc_q(spec.transpose(1, 2), spec_lengths, g=g)
# Compute z_p using flow
z_p = self.flow(z, spec_mask, g=g)
# Randomly slice z and compute o using dec
z_slice, ids_slice = commons.rand_slice_segments(z, spec_lengths, self.segment_size)
o = self.dec(z_slice, g=g)
return o, ids_slice, spec_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
@torch.no_grad()
def inference(self, c, g=None, mel=None, c_lengths=None):
"""
Inference pass of the model
Args:
c (torch.Tensor): Input tensor. Shape: (batch_size, c_seq_len).
g (torch.Tensor): Speaker embedding tensor. Shape: (batch_size, spk_emb_dim).
mel (torch.Tensor): Mel-spectrogram tensor. Shape: (batch_size, mel_seq_len, mel_dim).
c_lengths (torch.Tensor): Lengths of the input tensor. Shape: (batch_size,).
Returns:
torch.Tensor: Output tensor.
"""
if c_lengths == None:
c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
if not self.use_spk:
g = self.enc_spk.embed_utterance(mel)
g = g.unsqueeze(-1)
z_p, m_p, logs_p, c_mask = self.enc_p(c, c_lengths)
z = self.flow(z_p, c_mask, g=g, reverse=True)
o = self.dec(z * c_mask, g=g)
return o
def extract_wavlm_features(self, y):
"""Extract WavLM features from an audio tensor.
Args:
y (torch.Tensor): Audio tensor. Shape: (batch_size, audio_seq_len).
"""
with torch.no_grad():
c = self.wavlm.extract_features(y)[0]
c = c.transpose(1, 2)
return c
def load_audio(self, wav):
"""Read and format the input audio."""
if isinstance(wav, str):
wav, _ = librosa.load(wav, sr=self.config.audio.input_sample_rate)
if isinstance(wav, np.ndarray):
wav = torch.from_numpy(wav).to(self.device)
if isinstance(wav, torch.Tensor):
wav = wav.to(self.device)
if isinstance(wav, list):
wav = torch.from_numpy(np.array(wav)).to(self.device)
return wav.float()
@torch.inference_mode()
def voice_conversion(self, src, tgt):
"""
Voice conversion pass of the model.
Args:
src (str or torch.Tensor): Source utterance.
tgt (str or torch.Tensor): Target utterance.
Returns:
torch.Tensor: Output tensor.
"""
wav_tgt = self.load_audio(tgt).cpu().numpy()
wav_tgt, _ = librosa.effects.trim(wav_tgt, top_db=20)
if self.config.model_args.use_spk:
g_tgt = self.enc_spk_ex.embed_utterance(wav_tgt)
g_tgt = torch.from_numpy(g_tgt)[None, :, None].to(self.device)
else:
wav_tgt = torch.from_numpy(wav_tgt).unsqueeze(0).to(self.device)
mel_tgt = mel_spectrogram_torch(
wav_tgt,
self.config.audio.filter_length,
self.config.audio.n_mel_channels,
self.config.audio.input_sample_rate,
self.config.audio.hop_length,
self.config.audio.win_length,
self.config.audio.mel_fmin,
self.config.audio.mel_fmax,
)
# src
wav_src = self.load_audio(src)
c = self.extract_wavlm_features(wav_src[None, :])
if self.config.model_args.use_spk:
audio = self.inference(c, g=g_tgt)
else:
audio = self.inference(c, mel=mel_tgt.transpose(1, 2))
audio = audio[0][0].data.cpu().float().numpy()
return audio
def eval_step():
...
@staticmethod
def init_from_config(config: "VitsConfig", samples: Union[List[List], List[Dict]] = None, verbose=True):
model = FreeVC(config)
return model
def load_checkpoint(self, config, checkpoint_path, eval=False, strict=True, cache=False):
state = load_fsspec(checkpoint_path, map_location=torch.device("cpu"), cache=cache)
self.load_state_dict(state["model"], strict=strict)
if eval:
self.eval()
def train_step():
...
@dataclass
class FreeVCConfig(BaseVCConfig):
"""Defines parameters for FreeVC End2End TTS model.
Args:
model (str):
Model name. Do not change unless you know what you are doing.
model_args (FreeVCArgs):
Model architecture arguments. Defaults to `FreeVCArgs()`.
audio (FreeVCAudioConfig):
Audio processing configuration. Defaults to `FreeVCAudioConfig()`.
grad_clip (List):
Gradient clipping thresholds for each optimizer. Defaults to `[1000.0, 1000.0]`.
lr_gen (float):
Initial learning rate for the generator. Defaults to 0.0002.
lr_disc (float):
Initial learning rate for the discriminator. Defaults to 0.0002.
lr_scheduler_gen (str):
Name of the learning rate scheduler for the generator. One of the `torch.optim.lr_scheduler.*`. Defaults to
`ExponentialLR`.
lr_scheduler_gen_params (dict):
Parameters for the learning rate scheduler of the generator. Defaults to `{'gamma': 0.999875, "last_epoch":-1}`.
lr_scheduler_disc (str):
Name of the learning rate scheduler for the discriminator. One of the `torch.optim.lr_scheduler.*`. Defaults to
`ExponentialLR`.
lr_scheduler_disc_params (dict):
Parameters for the learning rate scheduler of the discriminator. Defaults to `{'gamma': 0.999875, "last_epoch":-1}`.
scheduler_after_epoch (bool):
If true, step the schedulers after each epoch else after each step. Defaults to `False`.
optimizer (str):
Name of the optimizer to use with both the generator and the discriminator networks. One of the
`torch.optim.*`. Defaults to `AdamW`.
kl_loss_alpha (float):
Loss weight for KL loss. Defaults to 1.0.
disc_loss_alpha (float):
Loss weight for the discriminator loss. Defaults to 1.0.
gen_loss_alpha (float):
Loss weight for the generator loss. Defaults to 1.0.
feat_loss_alpha (float):
Loss weight for the feature matching loss. Defaults to 1.0.
mel_loss_alpha (float):
Loss weight for the mel loss. Defaults to 45.0.
return_wav (bool):
If true, data loader returns the waveform as well as the other outputs. Do not change. Defaults to `True`.
compute_linear_spec (bool):
If true, the linear spectrogram is computed and returned alongside the mel output. Do not change. Defaults to `True`.
use_weighted_sampler (bool):
If true, use weighted sampler with bucketing for balancing samples between datasets used in training. Defaults to `False`.
weighted_sampler_attrs (dict):
Key retuned by the formatter to be used for weighted sampler. For example `{"root_path": 2.0, "speaker_name": 1.0}` sets sample probabilities
by overweighting `root_path` by 2.0. Defaults to `{}`.
weighted_sampler_multipliers (dict):
Weight each unique value of a key returned by the formatter for weighted sampling.
For example `{"root_path":{"/raid/datasets/libritts-clean-16khz-bwe-coqui_44khz/LibriTTS/train-clean-100/":1.0, "/raid/datasets/libritts-clean-16khz-bwe-coqui_44khz/LibriTTS/train-clean-360/": 0.5}`.
It will sample instances from `train-clean-100` 2 times more than `train-clean-360`. Defaults to `{}`.
r (int):
Number of spectrogram frames to be generated at a time. Do not change. Defaults to `1`.
add_blank (bool):
If true, a blank token is added in between every character. Defaults to `True`.
test_sentences (List[List]):
List of sentences with speaker and language information to be used for testing.
language_ids_file (str):
Path to the language ids file.
use_language_embedding (bool):
If true, language embedding is used. Defaults to `False`.
Note:
Check :class:`TTS.tts.configs.shared_configs.BaseTTSConfig` for the inherited parameters.
Example:
>>> from TTS.tts.configs.freevc_config import FreeVCConfig
>>> config = FreeVCConfig()
"""
model: str = "freevc"
# model specific params
model_args: FreeVCArgs = FreeVCArgs()
audio: FreeVCAudioConfig = FreeVCAudioConfig()
# optimizer
# TODO with training support
# loss params
# TODO with training support
# data loader params
return_wav: bool = True
compute_linear_spec: bool = True
# sampler params
use_weighted_sampler: bool = False # TODO: move it to the base config
weighted_sampler_attrs: dict = field(default_factory=lambda: {})
weighted_sampler_multipliers: dict = field(default_factory=lambda: {})
# overrides
r: int = 1 # DO NOT CHANGE
add_blank: bool = True
# multi-speaker settings
# use speaker embedding layer
num_speakers: int = 0
speakers_file: str = None
speaker_embedding_channels: int = 256
# use d-vectors
use_d_vector_file: bool = False
d_vector_file: List[str] = None
d_vector_dim: int = None
def __post_init__(self):
for key, val in self.model_args.items():
if hasattr(self, key):
self[key] = val
|