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"""
Author: Speech Lab, Alibaba Group, China
"""
import logging
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 funasr_detach.layers.abs_normalize import AbsNormalize
from funasr_detach.losses.label_smoothing_loss import (
LabelSmoothingLoss, # noqa: H301
)
from funasr_detach.models.ctc import CTC
from funasr_detach.models.decoder.abs_decoder import AbsDecoder
from funasr_detach.models.encoder.abs_encoder import AbsEncoder
from funasr_detach.frontends.abs_frontend import AbsFrontend
from funasr_detach.models.postencoder.abs_postencoder import AbsPostEncoder
from funasr_detach.models.preencoder.abs_preencoder import AbsPreEncoder
from funasr_detach.models.specaug.abs_specaug import AbsSpecAug
from funasr_detach.models.transformer.utils.add_sos_eos import add_sos_eos
from funasr_detach.metrics import ErrorCalculator
from funasr_detach.metrics.compute_acc import th_accuracy
from funasr_detach.train_utils.device_funcs import force_gatherable
from funasr_detach.models.base_model import FunASRModel
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 ESPnetSVModel(FunASRModel):
"""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,
postencoder: Optional[AbsPostEncoder],
pooling_layer: torch.nn.Module,
decoder: AbsDecoder,
):
super().__init__()
# note that eos is the same as sos (equivalent ID)
self.vocab_size = vocab_size
self.token_list = token_list.copy()
self.frontend = frontend
self.specaug = specaug
self.normalize = normalize
self.preencoder = preencoder
self.postencoder = postencoder
self.encoder = encoder
self.pooling_layer = pooling_layer
self.decoder = decoder
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)
intermediate_outs = None
if isinstance(encoder_out, tuple):
intermediate_outs = encoder_out[1]
encoder_out = encoder_out[0]
loss_att, acc_att, cer_att, wer_att = None, None, None, None
loss_ctc, cer_ctc = None, None
loss_transducer, cer_transducer, wer_transducer = None, None, None
stats = dict()
# 1. CTC branch
if self.ctc_weight != 0.0:
loss_ctc, cer_ctc = self._calc_ctc_loss(
encoder_out, encoder_out_lens, text, text_lengths
)
# Collect CTC branch stats
stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None
stats["cer_ctc"] = cer_ctc
# Intermediate CTC (optional)
loss_interctc = 0.0
if self.interctc_weight != 0.0 and intermediate_outs is not None:
for layer_idx, intermediate_out in intermediate_outs:
# we assume intermediate_out has the same length & padding
# as those of encoder_out
loss_ic, cer_ic = self._calc_ctc_loss(
intermediate_out, encoder_out_lens, text, text_lengths
)
loss_interctc = loss_interctc + loss_ic
# Collect Intermedaite CTC stats
stats["loss_interctc_layer{}".format(layer_idx)] = (
loss_ic.detach() if loss_ic is not None else None
)
stats["cer_interctc_layer{}".format(layer_idx)] = cer_ic
loss_interctc = loss_interctc / len(intermediate_outs)
# calculate whole encoder loss
loss_ctc = (
1 - self.interctc_weight
) * loss_ctc + self.interctc_weight * loss_interctc
if self.use_transducer_decoder:
# 2a. Transducer decoder branch
(
loss_transducer,
cer_transducer,
wer_transducer,
) = self._calc_transducer_loss(
encoder_out,
encoder_out_lens,
text,
)
if loss_ctc is not None:
loss = loss_transducer + (self.ctc_weight * loss_ctc)
else:
loss = loss_transducer
# Collect Transducer branch stats
stats["loss_transducer"] = (
loss_transducer.detach() if loss_transducer is not None else None
)
stats["cer_transducer"] = cer_transducer
stats["wer_transducer"] = wer_transducer
else:
# 2b. Attention decoder branch
if self.ctc_weight != 1.0:
loss_att, acc_att, cer_att, wer_att = self._calc_att_loss(
encoder_out, encoder_out_lens, text, text_lengths
)
# 3. CTC-Att loss definition
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
# Collect Attn branch stats
stats["loss_att"] = loss_att.detach() if loss_att is not None else None
stats["acc"] = acc_att
stats["cer"] = cer_att
stats["wer"] = wer_att
# Collect total loss stats
stats["loss"] = torch.clone(loss.detach())
# 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]:
if self.extract_feats_in_collect_stats:
feats, feats_lengths = self._extract_feats(speech, speech_lengths)
else:
# Generate dummy stats if extract_feats_in_collect_stats is False
logging.warning(
"Generating dummy stats for feats and feats_lengths, "
"because encoder_conf.extract_feats_in_collect_stats is "
f"{self.extract_feats_in_collect_stats}"
)
feats, feats_lengths = 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) -> (Batch, Channel, Length2, Dim2)
encoder_out, encoder_out_lens = self.encoder(feats, feats_lengths)
# Post-encoder, e.g. NLU
if self.postencoder is not None:
encoder_out, encoder_out_lens = self.postencoder(
encoder_out, encoder_out_lens
)
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
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