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from contextlib import contextmanager
from distutils.version import LooseVersion
from typing import Dict
from typing import List
from typing import Optional
from typing import Tuple
from typing import Union
import torch
from typeguard import check_argument_types
from espnet.nets.e2e_asr_common import ErrorCalculator
from espnet.nets.pytorch_backend.nets_utils import th_accuracy
from espnet.nets.pytorch_backend.transformer.add_sos_eos import add_sos_eos
from espnet.nets.pytorch_backend.transformer.label_smoothing_loss import (
LabelSmoothingLoss, # noqa: H301
)
from espnet2.asr.ctc import CTC
from espnet2.asr.decoder.abs_decoder import AbsDecoder
from espnet2.asr.encoder.abs_encoder import AbsEncoder
from espnet2.asr.frontend.abs_frontend import AbsFrontend
from espnet2.asr.preencoder.abs_preencoder import AbsPreEncoder
from espnet2.asr.specaug.abs_specaug import AbsSpecAug
from espnet2.layers.abs_normalize import AbsNormalize
from espnet2.torch_utils.device_funcs import force_gatherable
from espnet2.train.abs_espnet_model import AbsESPnetModel
if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
from torch.cuda.amp import autocast
else:
# Nothing to do if torch<1.6.0
@contextmanager
def autocast(enabled=True):
yield
class ESPnetASRModel(AbsESPnetModel):
"""CTC-attention hybrid Encoder-Decoder model"""
def __init__(
self,
vocab_size: int,
token_list: Union[Tuple[str, ...], List[str]],
frontend: Optional[AbsFrontend],
specaug: Optional[AbsSpecAug],
normalize: Optional[AbsNormalize],
preencoder: Optional[AbsPreEncoder],
encoder: AbsEncoder,
decoder: AbsDecoder,
ctc: CTC,
rnnt_decoder: None,
ctc_weight: float = 0.5,
ignore_id: int = -1,
lsm_weight: float = 0.0,
length_normalized_loss: bool = False,
report_cer: bool = True,
report_wer: bool = True,
sym_space: str = "<space>",
sym_blank: str = "<blank>",
):
assert check_argument_types()
assert 0.0 <= ctc_weight <= 1.0, ctc_weight
assert rnnt_decoder is None, "Not implemented"
super().__init__()
# note that eos is the same as sos (equivalent ID)
self.sos = vocab_size - 1
self.eos = vocab_size - 1
self.vocab_size = vocab_size
self.ignore_id = ignore_id
self.ctc_weight = ctc_weight
self.token_list = token_list.copy()
self.frontend = frontend
self.specaug = specaug
self.normalize = normalize
self.preencoder = preencoder
self.encoder = encoder
self.decoder = decoder
if ctc_weight == 0.0:
self.ctc = None
else:
self.ctc = ctc
self.rnnt_decoder = rnnt_decoder
self.criterion_att = LabelSmoothingLoss(
size=vocab_size,
padding_idx=ignore_id,
smoothing=lsm_weight,
normalize_length=length_normalized_loss,
)
if report_cer or report_wer:
self.error_calculator = ErrorCalculator(
token_list, sym_space, sym_blank, report_cer, report_wer
)
else:
self.error_calculator = None
def forward(
self,
speech: torch.Tensor,
speech_lengths: torch.Tensor,
text: torch.Tensor,
text_lengths: torch.Tensor,
) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
"""Frontend + Encoder + Decoder + Calc loss
Args:
speech: (Batch, Length, ...)
speech_lengths: (Batch, )
text: (Batch, Length)
text_lengths: (Batch,)
"""
assert text_lengths.dim() == 1, text_lengths.shape
# Check that batch_size is unified
assert (
speech.shape[0]
== speech_lengths.shape[0]
== text.shape[0]
== text_lengths.shape[0]
), (speech.shape, speech_lengths.shape, text.shape, text_lengths.shape)
batch_size = speech.shape[0]
# for data-parallel
text = text[:, : text_lengths.max()]
# 1. Encoder
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
# 2a. Attention-decoder branch
if self.ctc_weight == 1.0:
loss_att, acc_att, cer_att, wer_att = None, None, None, None
else:
loss_att, acc_att, cer_att, wer_att = self._calc_att_loss(
encoder_out, encoder_out_lens, text, text_lengths
)
# 2b. CTC branch
if self.ctc_weight == 0.0:
loss_ctc, cer_ctc = None, None
else:
loss_ctc, cer_ctc = self._calc_ctc_loss(
encoder_out, encoder_out_lens, text, text_lengths
)
# 2c. RNN-T branch
if self.rnnt_decoder is not None:
_ = self._calc_rnnt_loss(encoder_out, encoder_out_lens, text, text_lengths)
if self.ctc_weight == 0.0:
loss = loss_att
elif self.ctc_weight == 1.0:
loss = loss_ctc
else:
loss = self.ctc_weight * loss_ctc + (1 - self.ctc_weight) * loss_att
stats = dict(
loss=loss.detach(),
loss_att=loss_att.detach() if loss_att is not None else None,
loss_ctc=loss_ctc.detach() if loss_ctc is not None else None,
acc=acc_att,
cer=cer_att,
wer=wer_att,
cer_ctc=cer_ctc,
)
# force_gatherable: to-device and to-tensor if scalar for DataParallel
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
return loss, stats, weight
def collect_feats(
self,
speech: torch.Tensor,
speech_lengths: torch.Tensor,
text: torch.Tensor,
text_lengths: torch.Tensor,
) -> Dict[str, torch.Tensor]:
feats, feats_lengths = self._extract_feats(speech, speech_lengths)
return {"feats": feats, "feats_lengths": feats_lengths}
def encode(
self, speech: torch.Tensor, speech_lengths: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Frontend + Encoder. Note that this method is used by asr_inference.py
Args:
speech: (Batch, Length, ...)
speech_lengths: (Batch, )
"""
with autocast(False):
# 1. Extract feats
feats, feats_lengths = self._extract_feats(speech, speech_lengths)
# 2. Data augmentation
if self.specaug is not None and self.training:
feats, feats_lengths = self.specaug(feats, feats_lengths)
# 3. Normalization for feature: e.g. Global-CMVN, Utterance-CMVN
if self.normalize is not None:
feats, feats_lengths = self.normalize(feats, feats_lengths)
# Pre-encoder, e.g. used for raw input data
if self.preencoder is not None:
feats, feats_lengths = self.preencoder(feats, feats_lengths)
# 4. Forward encoder
# feats: (Batch, Length, Dim)
# -> encoder_out: (Batch, Length2, Dim2)
encoder_out, encoder_out_lens, _ = self.encoder(feats, feats_lengths)
assert encoder_out.size(0) == speech.size(0), (
encoder_out.size(),
speech.size(0),
)
assert encoder_out.size(1) <= encoder_out_lens.max(), (
encoder_out.size(),
encoder_out_lens.max(),
)
return encoder_out, encoder_out_lens
def _extract_feats(
self, speech: torch.Tensor, speech_lengths: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
assert speech_lengths.dim() == 1, speech_lengths.shape
# for data-parallel
speech = speech[:, : speech_lengths.max()]
if self.frontend is not None:
# Frontend
# e.g. STFT and Feature extract
# data_loader may send time-domain signal in this case
# speech (Batch, NSamples) -> feats: (Batch, NFrames, Dim)
feats, feats_lengths = self.frontend(speech, speech_lengths)
else:
# No frontend and no feature extract
feats, feats_lengths = speech, speech_lengths
return feats, feats_lengths
def _calc_att_loss(
self,
encoder_out: torch.Tensor,
encoder_out_lens: torch.Tensor,
ys_pad: torch.Tensor,
ys_pad_lens: torch.Tensor,
):
ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
ys_in_lens = ys_pad_lens + 1
# 1. Forward decoder
decoder_out, _ = self.decoder(
encoder_out, encoder_out_lens, ys_in_pad, ys_in_lens
)
# 2. Compute attention loss
loss_att = self.criterion_att(decoder_out, ys_out_pad)
acc_att = th_accuracy(
decoder_out.view(-1, self.vocab_size),
ys_out_pad,
ignore_label=self.ignore_id,
)
# Compute cer/wer using attention-decoder
if self.training or self.error_calculator is None:
cer_att, wer_att = None, None
else:
ys_hat = decoder_out.argmax(dim=-1)
cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu())
return loss_att, acc_att, cer_att, wer_att
def _calc_ctc_loss(
self,
encoder_out: torch.Tensor,
encoder_out_lens: torch.Tensor,
ys_pad: torch.Tensor,
ys_pad_lens: torch.Tensor,
):
# Calc CTC loss
loss_ctc = self.ctc(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens)
# Calc CER using CTC
cer_ctc = None
if not self.training and self.error_calculator is not None:
ys_hat = self.ctc.argmax(encoder_out).data
cer_ctc = self.error_calculator(ys_hat.cpu(), ys_pad.cpu(), is_ctc=True)
return loss_ctc, cer_ctc
def _calc_rnnt_loss(
self,
encoder_out: torch.Tensor,
encoder_out_lens: torch.Tensor,
ys_pad: torch.Tensor,
ys_pad_lens: torch.Tensor,
):
raise NotImplementedError
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