File size: 19,112 Bytes
ad16788 |
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 |
"""Transducer speech recognition model (pytorch)."""
from argparse import Namespace
from collections import Counter
from dataclasses import asdict
import logging
import math
import numpy
import chainer
import torch
from espnet.nets.asr_interface import ASRInterface
from espnet.nets.pytorch_backend.ctc import ctc_for
from espnet.nets.pytorch_backend.nets_utils import get_subsample
from espnet.nets.pytorch_backend.nets_utils import make_non_pad_mask
from espnet.nets.pytorch_backend.transducer.arguments import (
add_encoder_general_arguments, # noqa: H301
add_rnn_encoder_arguments, # noqa: H301
add_custom_encoder_arguments, # noqa: H301
add_decoder_general_arguments, # noqa: H301
add_rnn_decoder_arguments, # noqa: H301
add_custom_decoder_arguments, # noqa: H301
add_custom_training_arguments, # noqa: H301
add_transducer_arguments, # noqa: H301
add_auxiliary_task_arguments, # noqa: H301
)
from espnet.nets.pytorch_backend.transducer.auxiliary_task import AuxiliaryTask
from espnet.nets.pytorch_backend.transducer.custom_decoder import CustomDecoder
from espnet.nets.pytorch_backend.transducer.custom_encoder import CustomEncoder
from espnet.nets.pytorch_backend.transducer.error_calculator import ErrorCalculator
from espnet.nets.pytorch_backend.transducer.initializer import initializer
from espnet.nets.pytorch_backend.transducer.joint_network import JointNetwork
from espnet.nets.pytorch_backend.transducer.loss import TransLoss
from espnet.nets.pytorch_backend.transducer.rnn_decoder import DecoderRNNT
from espnet.nets.pytorch_backend.transducer.rnn_encoder import encoder_for
from espnet.nets.pytorch_backend.transducer.utils import prepare_loss_inputs
from espnet.nets.pytorch_backend.transducer.utils import valid_aux_task_layer_list
from espnet.nets.pytorch_backend.transformer.attention import (
MultiHeadedAttention, # noqa: H301
RelPositionMultiHeadedAttention, # noqa: H301
)
from espnet.nets.pytorch_backend.transformer.label_smoothing_loss import (
LabelSmoothingLoss, # noqa: H301
)
from espnet.nets.pytorch_backend.transformer.mask import target_mask
from espnet.nets.pytorch_backend.transformer.plot import PlotAttentionReport
from espnet.utils.fill_missing_args import fill_missing_args
class Reporter(chainer.Chain):
"""A chainer reporter wrapper for transducer models."""
def report(
self,
loss,
loss_trans,
loss_ctc,
loss_lm,
loss_aux_trans,
loss_aux_symm_kl,
cer,
wer,
):
"""Instantiate reporter attributes."""
chainer.reporter.report({"loss": loss}, self)
chainer.reporter.report({"loss_trans": loss_trans}, self)
chainer.reporter.report({"loss_ctc": loss_ctc}, self)
chainer.reporter.report({"loss_lm": loss_lm}, self)
chainer.reporter.report({"loss_aux_trans": loss_aux_trans}, self)
chainer.reporter.report({"loss_aux_symm_kl": loss_aux_symm_kl}, self)
chainer.reporter.report({"cer": cer}, self)
chainer.reporter.report({"wer": wer}, self)
logging.info("loss:" + str(loss))
class E2E(ASRInterface, torch.nn.Module):
"""E2E module for transducer models.
Args:
idim (int): dimension of inputs
odim (int): dimension of outputs
args (Namespace): argument Namespace containing options
ignore_id (int): padding symbol id
blank_id (int): blank symbol id
"""
@staticmethod
def add_arguments(parser):
"""Add arguments for transducer model."""
E2E.encoder_add_general_arguments(parser)
E2E.encoder_add_rnn_arguments(parser)
E2E.encoder_add_custom_arguments(parser)
E2E.decoder_add_general_arguments(parser)
E2E.decoder_add_rnn_arguments(parser)
E2E.decoder_add_custom_arguments(parser)
E2E.training_add_custom_arguments(parser)
E2E.transducer_add_arguments(parser)
E2E.auxiliary_task_add_arguments(parser)
return parser
@staticmethod
def encoder_add_general_arguments(parser):
"""Add general arguments for encoder."""
group = parser.add_argument_group("Encoder general arguments")
group = add_encoder_general_arguments(group)
return parser
@staticmethod
def encoder_add_rnn_arguments(parser):
"""Add arguments for RNN encoder."""
group = parser.add_argument_group("RNN encoder arguments")
group = add_rnn_encoder_arguments(group)
return parser
@staticmethod
def encoder_add_custom_arguments(parser):
"""Add arguments for Custom encoder."""
group = parser.add_argument_group("Custom encoder arguments")
group = add_custom_encoder_arguments(group)
return parser
@staticmethod
def decoder_add_general_arguments(parser):
"""Add general arguments for decoder."""
group = parser.add_argument_group("Decoder general arguments")
group = add_decoder_general_arguments(group)
return parser
@staticmethod
def decoder_add_rnn_arguments(parser):
"""Add arguments for RNN decoder."""
group = parser.add_argument_group("RNN decoder arguments")
group = add_rnn_decoder_arguments(group)
return parser
@staticmethod
def decoder_add_custom_arguments(parser):
"""Add arguments for Custom decoder."""
group = parser.add_argument_group("Custom decoder arguments")
group = add_custom_decoder_arguments(group)
return parser
@staticmethod
def training_add_custom_arguments(parser):
"""Add arguments for Custom architecture training."""
group = parser.add_argument_group("Training arguments for custom archictecture")
group = add_custom_training_arguments(group)
return parser
@staticmethod
def transducer_add_arguments(parser):
"""Add arguments for transducer model."""
group = parser.add_argument_group("Transducer model arguments")
group = add_transducer_arguments(group)
return parser
@staticmethod
def auxiliary_task_add_arguments(parser):
"""Add arguments for auxiliary task."""
group = parser.add_argument_group("Auxiliary task arguments")
group = add_auxiliary_task_arguments(group)
return parser
@property
def attention_plot_class(self):
"""Get attention plot class."""
return PlotAttentionReport
def get_total_subsampling_factor(self):
"""Get total subsampling factor."""
if self.etype == "custom":
return self.encoder.conv_subsampling_factor * int(
numpy.prod(self.subsample)
)
else:
return self.enc.conv_subsampling_factor * int(numpy.prod(self.subsample))
def __init__(self, idim, odim, args, ignore_id=-1, blank_id=0, training=True):
"""Construct an E2E object for transducer model."""
torch.nn.Module.__init__(self)
args = fill_missing_args(args, self.add_arguments)
self.is_rnnt = True
self.transducer_weight = args.transducer_weight
self.use_aux_task = (
True if (args.aux_task_type is not None and training) else False
)
self.use_aux_ctc = args.aux_ctc and training
self.aux_ctc_weight = args.aux_ctc_weight
self.use_aux_cross_entropy = args.aux_cross_entropy and training
self.aux_cross_entropy_weight = args.aux_cross_entropy_weight
if self.use_aux_task:
n_layers = (
(len(args.enc_block_arch) * args.enc_block_repeat - 1)
if args.enc_block_arch is not None
else (args.elayers - 1)
)
aux_task_layer_list = valid_aux_task_layer_list(
args.aux_task_layer_list,
n_layers,
)
else:
aux_task_layer_list = []
if "custom" in args.etype:
if args.enc_block_arch is None:
raise ValueError(
"When specifying custom encoder type, --enc-block-arch"
"should also be specified in training config. See"
"egs/vivos/asr1/conf/transducer/train_*.yaml for more info."
)
self.subsample = get_subsample(args, mode="asr", arch="transformer")
self.encoder = CustomEncoder(
idim,
args.enc_block_arch,
input_layer=args.custom_enc_input_layer,
repeat_block=args.enc_block_repeat,
self_attn_type=args.custom_enc_self_attn_type,
positional_encoding_type=args.custom_enc_positional_encoding_type,
positionwise_activation_type=args.custom_enc_pw_activation_type,
conv_mod_activation_type=args.custom_enc_conv_mod_activation_type,
aux_task_layer_list=aux_task_layer_list,
)
encoder_out = self.encoder.enc_out
self.most_dom_list = args.enc_block_arch[:]
else:
self.subsample = get_subsample(args, mode="asr", arch="rnn-t")
self.enc = encoder_for(
args,
idim,
self.subsample,
aux_task_layer_list=aux_task_layer_list,
)
encoder_out = args.eprojs
if "custom" in args.dtype:
if args.dec_block_arch is None:
raise ValueError(
"When specifying custom decoder type, --dec-block-arch"
"should also be specified in training config. See"
"egs/vivos/asr1/conf/transducer/train_*.yaml for more info."
)
self.decoder = CustomDecoder(
odim,
args.dec_block_arch,
input_layer=args.custom_dec_input_layer,
repeat_block=args.dec_block_repeat,
positionwise_activation_type=args.custom_dec_pw_activation_type,
dropout_rate_embed=args.dropout_rate_embed_decoder,
)
decoder_out = self.decoder.dunits
if "custom" in args.etype:
self.most_dom_list += args.dec_block_arch[:]
else:
self.most_dom_list = args.dec_block_arch[:]
else:
self.dec = DecoderRNNT(
odim,
args.dtype,
args.dlayers,
args.dunits,
blank_id,
args.dec_embed_dim,
args.dropout_rate_decoder,
args.dropout_rate_embed_decoder,
)
decoder_out = args.dunits
self.joint_network = JointNetwork(
odim, encoder_out, decoder_out, args.joint_dim, args.joint_activation_type
)
if hasattr(self, "most_dom_list"):
self.most_dom_dim = sorted(
Counter(
d["d_hidden"] for d in self.most_dom_list if "d_hidden" in d
).most_common(),
key=lambda x: x[0],
reverse=True,
)[0][0]
self.etype = args.etype
self.dtype = args.dtype
self.sos = odim - 1
self.eos = odim - 1
self.blank_id = blank_id
self.ignore_id = ignore_id
self.space = args.sym_space
self.blank = args.sym_blank
self.odim = odim
self.reporter = Reporter()
self.error_calculator = None
self.default_parameters(args)
if training:
self.criterion = TransLoss(args.trans_type, self.blank_id)
decoder = self.decoder if self.dtype == "custom" else self.dec
if args.report_cer or args.report_wer:
self.error_calculator = ErrorCalculator(
decoder,
self.joint_network,
args.char_list,
args.sym_space,
args.sym_blank,
args.report_cer,
args.report_wer,
)
if self.use_aux_task:
self.auxiliary_task = AuxiliaryTask(
decoder,
self.joint_network,
self.criterion,
args.aux_task_type,
args.aux_task_weight,
encoder_out,
args.joint_dim,
)
if self.use_aux_ctc:
self.aux_ctc = ctc_for(
Namespace(
num_encs=1,
eprojs=encoder_out,
dropout_rate=args.aux_ctc_dropout_rate,
ctc_type="warpctc",
),
odim,
)
if self.use_aux_cross_entropy:
self.aux_decoder_output = torch.nn.Linear(decoder_out, odim)
self.aux_cross_entropy = LabelSmoothingLoss(
odim, ignore_id, args.aux_cross_entropy_smoothing
)
self.loss = None
self.rnnlm = None
def default_parameters(self, args):
"""Initialize/reset parameters for transducer.
Args:
args (Namespace): argument Namespace containing options
"""
initializer(self, args)
def forward(self, xs_pad, ilens, ys_pad):
"""E2E forward.
Args:
xs_pad (torch.Tensor): batch of padded source sequences (B, Tmax, idim)
ilens (torch.Tensor): batch of lengths of input sequences (B)
ys_pad (torch.Tensor): batch of padded target sequences (B, Lmax)
Returns:
loss (torch.Tensor): transducer loss value
"""
# 1. encoder
xs_pad = xs_pad[:, : max(ilens)]
if "custom" in self.etype:
src_mask = make_non_pad_mask(ilens.tolist()).to(xs_pad.device).unsqueeze(-2)
_hs_pad, hs_mask = self.encoder(xs_pad, src_mask)
else:
_hs_pad, hs_mask, _ = self.enc(xs_pad, ilens)
if self.use_aux_task:
hs_pad, aux_hs_pad = _hs_pad[0], _hs_pad[1]
else:
hs_pad, aux_hs_pad = _hs_pad, None
# 1.5. transducer preparation related
ys_in_pad, ys_out_pad, target, pred_len, target_len = prepare_loss_inputs(
ys_pad, hs_mask
)
# 2. decoder
if "custom" in self.dtype:
ys_mask = target_mask(ys_in_pad, self.blank_id)
pred_pad, _ = self.decoder(ys_in_pad, ys_mask, hs_pad)
else:
pred_pad = self.dec(hs_pad, ys_in_pad)
z = self.joint_network(hs_pad.unsqueeze(2), pred_pad.unsqueeze(1))
# 3. loss computation
loss_trans = self.criterion(z, target, pred_len, target_len)
if self.use_aux_task and aux_hs_pad is not None:
loss_aux_trans, loss_aux_symm_kl = self.auxiliary_task(
aux_hs_pad, pred_pad, z, target, pred_len, target_len
)
else:
loss_aux_trans, loss_aux_symm_kl = 0.0, 0.0
if self.use_aux_ctc:
if "custom" in self.etype:
hs_mask = torch.IntTensor(
[h.size(1) for h in hs_mask],
).to(hs_mask.device)
loss_ctc = self.aux_ctc_weight * self.aux_ctc(hs_pad, hs_mask, ys_pad)
else:
loss_ctc = 0.0
if self.use_aux_cross_entropy:
loss_lm = self.aux_cross_entropy_weight * self.aux_cross_entropy(
self.aux_decoder_output(pred_pad), ys_out_pad
)
else:
loss_lm = 0.0
loss = (
loss_trans
+ self.transducer_weight * (loss_aux_trans + loss_aux_symm_kl)
+ loss_ctc
+ loss_lm
)
self.loss = loss
loss_data = float(loss)
# 4. compute cer/wer
if self.training or self.error_calculator is None:
cer, wer = None, None
else:
cer, wer = self.error_calculator(hs_pad, ys_pad)
if not math.isnan(loss_data):
self.reporter.report(
loss_data,
float(loss_trans),
float(loss_ctc),
float(loss_lm),
float(loss_aux_trans),
float(loss_aux_symm_kl),
cer,
wer,
)
else:
logging.warning("loss (=%f) is not correct", loss_data)
return self.loss
def encode_custom(self, x):
"""Encode acoustic features.
Args:
x (ndarray): input acoustic feature (T, D)
Returns:
x (torch.Tensor): encoded features (T, D_enc)
"""
x = torch.as_tensor(x).unsqueeze(0)
enc_output, _ = self.encoder(x, None)
return enc_output.squeeze(0)
def encode_rnn(self, x):
"""Encode acoustic features.
Args:
x (ndarray): input acoustic feature (T, D)
Returns:
x (torch.Tensor): encoded features (T, D_enc)
"""
p = next(self.parameters())
ilens = [x.shape[0]]
x = x[:: self.subsample[0], :]
h = torch.as_tensor(x, device=p.device, dtype=p.dtype)
hs = h.contiguous().unsqueeze(0)
hs, _, _ = self.enc(hs, ilens)
return hs.squeeze(0)
def recognize(self, x, beam_search):
"""Recognize input features.
Args:
x (ndarray): input acoustic feature (T, D)
beam_search (class): beam search class
Returns:
nbest_hyps (list): n-best decoding results
"""
self.eval()
if "custom" in self.etype:
h = self.encode_custom(x)
else:
h = self.encode_rnn(x)
nbest_hyps = beam_search(h)
return [asdict(n) for n in nbest_hyps]
def calculate_all_attentions(self, xs_pad, ilens, ys_pad):
"""E2E attention calculation.
Args:
xs_pad (torch.Tensor): batch of padded input sequences (B, Tmax, idim)
ilens (torch.Tensor): batch of lengths of input sequences (B)
ys_pad (torch.Tensor):
batch of padded character id sequence tensor (B, Lmax)
Returns:
ret (ndarray): attention weights with the following shape,
1) multi-head case => attention weights (B, H, Lmax, Tmax),
2) other case => attention weights (B, Lmax, Tmax).
"""
self.eval()
if "custom" not in self.etype and "custom" not in self.dtype:
return []
else:
with torch.no_grad():
self.forward(xs_pad, ilens, ys_pad)
ret = dict()
for name, m in self.named_modules():
if isinstance(m, MultiHeadedAttention) or isinstance(
m, RelPositionMultiHeadedAttention
):
ret[name] = m.attn.cpu().numpy()
self.train()
return ret
|