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
|
import torch.nn as nn
|
import random
|
import numpy as np
|
|
# from funasr.layers.abs_normalize import AbsNormalize
|
from funasr.losses.label_smoothing_loss import (
|
LabelSmoothingLoss, # noqa: H301
|
)
|
# from funasr.models.ctc import CTC
|
# from funasr.models.decoder.abs_decoder import AbsDecoder
|
# from funasr.models.e2e_asr_common import ErrorCalculator
|
# from funasr.models.encoder.abs_encoder import AbsEncoder
|
# from funasr.models.frontend.abs_frontend import AbsFrontend
|
# from funasr.models.postencoder.abs_postencoder import AbsPostEncoder
|
from funasr.models.predictor.cif import mae_loss
|
# from funasr.models.preencoder.abs_preencoder import AbsPreEncoder
|
# from funasr.models.specaug.abs_specaug import AbsSpecAug
|
from funasr.modules.add_sos_eos import add_sos_eos
|
from funasr.modules.nets_utils import make_pad_mask, pad_list
|
from funasr.modules.nets_utils import th_accuracy
|
from funasr.torch_utils.device_funcs import force_gatherable
|
# from funasr.models.base_model import FunASRModel
|
# from funasr.models.predictor.cif import CifPredictorV3
|
|
from funasr.cli.model_class_factory import *
|
|
|
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 Paraformer(nn.Module):
|
"""
|
Author: Speech Lab of DAMO Academy, Alibaba Group
|
Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
|
https://arxiv.org/abs/2206.08317
|
"""
|
|
def __init__(
|
self,
|
# token_list: Union[Tuple[str, ...], List[str]],
|
frontend: Optional[str] = None,
|
frontend_conf: Optional[Dict] = None,
|
specaug: Optional[str] = None,
|
specaug_conf: Optional[Dict] = None,
|
normalize: str = None,
|
normalize_conf: Optional[Dict] = None,
|
encoder: str = None,
|
encoder_conf: Optional[Dict] = None,
|
decoder: str = None,
|
decoder_conf: Optional[Dict] = None,
|
ctc: str = None,
|
ctc_conf: Optional[Dict] = None,
|
predictor: str = None,
|
predictor_conf: Optional[Dict] = None,
|
ctc_weight: float = 0.5,
|
interctc_weight: float = 0.0,
|
input_size: int = 80,
|
vocab_size: int = -1,
|
ignore_id: int = -1,
|
blank_id: int = 0,
|
sos: int = 1,
|
eos: int = 2,
|
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>",
|
# extract_feats_in_collect_stats: bool = True,
|
# predictor=None,
|
predictor_weight: float = 0.0,
|
predictor_bias: int = 0,
|
sampling_ratio: float = 0.2,
|
share_embedding: bool = False,
|
# preencoder: Optional[AbsPreEncoder] = None,
|
# postencoder: Optional[AbsPostEncoder] = None,
|
use_1st_decoder_loss: bool = False,
|
**kwargs,
|
):
|
assert 0.0 <= ctc_weight <= 1.0, ctc_weight
|
assert 0.0 <= interctc_weight < 1.0, interctc_weight
|
|
super().__init__()
|
|
# import pdb;
|
# pdb.set_trace()
|
|
if frontend is not None:
|
frontend_class = frontend_choices.get_class(frontend)
|
frontend = frontend_class(**frontend_conf)
|
if specaug is not None:
|
specaug_class = specaug_choices.get_class(specaug)
|
specaug = specaug_class(**specaug_conf)
|
if normalize is not None:
|
normalize_class = normalize_choices.get_class(normalize)
|
normalize = normalize_class(**normalize_conf)
|
encoder_class = encoder_choices.get_class(encoder)
|
encoder = encoder_class(input_size=input_size, **encoder_conf)
|
encoder_output_size = encoder.output_size()
|
if decoder is not None:
|
decoder_class = decoder_choices.get_class(decoder)
|
decoder = decoder_class(
|
vocab_size=vocab_size,
|
encoder_output_size=encoder_output_size,
|
**decoder_conf,
|
)
|
if ctc_weight > 0.0:
|
|
if ctc_conf is None:
|
ctc_conf = {}
|
|
ctc = CTC(
|
odim=vocab_size, encoder_output_size=encoder_output_size, **ctc_conf
|
)
|
if predictor is not None:
|
predictor_class = predictor_choices.get_class(predictor)
|
predictor = predictor_class(**predictor_conf)
|
|
# note that eos is the same as sos (equivalent ID)
|
self.blank_id = blank_id
|
self.sos = sos if sos is not None else vocab_size - 1
|
self.eos = eos if eos is not None else vocab_size - 1
|
self.vocab_size = vocab_size
|
self.ignore_id = ignore_id
|
self.ctc_weight = ctc_weight
|
self.interctc_weight = interctc_weight
|
# self.token_list = token_list.copy()
|
#
|
self.frontend = frontend
|
self.specaug = specaug
|
self.normalize = normalize
|
# self.preencoder = preencoder
|
# self.postencoder = postencoder
|
self.encoder = encoder
|
#
|
# if not hasattr(self.encoder, "interctc_use_conditioning"):
|
# self.encoder.interctc_use_conditioning = False
|
# if self.encoder.interctc_use_conditioning:
|
# self.encoder.conditioning_layer = torch.nn.Linear(
|
# vocab_size, self.encoder.output_size()
|
# )
|
#
|
# self.error_calculator = None
|
#
|
if ctc_weight == 1.0:
|
self.decoder = None
|
else:
|
self.decoder = 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
|
# )
|
#
|
if ctc_weight == 0.0:
|
self.ctc = None
|
else:
|
self.ctc = ctc
|
#
|
# self.extract_feats_in_collect_stats = extract_feats_in_collect_stats
|
self.predictor = predictor
|
self.predictor_weight = predictor_weight
|
self.predictor_bias = predictor_bias
|
self.sampling_ratio = sampling_ratio
|
self.criterion_pre = mae_loss(normalize_length=length_normalized_loss)
|
# self.step_cur = 0
|
#
|
self.share_embedding = share_embedding
|
if self.share_embedding:
|
self.decoder.embed = None
|
|
self.use_1st_decoder_loss = use_1st_decoder_loss
|
self.length_normalized_loss = length_normalized_loss
|
|
def forward(
|
self,
|
speech: torch.Tensor,
|
speech_lengths: torch.Tensor,
|
text: torch.Tensor,
|
text_lengths: torch.Tensor,
|
**kwargs,
|
) -> 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,)
|
decoding_ind: int
|
"""
|
decoding_ind = kwargs.get("kwargs", None)
|
# import pdb;
|
# pdb.set_trace()
|
if len(text_lengths.size()) > 1:
|
text_lengths = text_lengths[:, 0]
|
if len(speech_lengths.size()) > 1:
|
speech_lengths = speech_lengths[:, 0]
|
|
batch_size = speech.shape[0]
|
|
# # for data-parallel
|
# text = text[:, : text_lengths.max()]
|
# speech = speech[:, :speech_lengths.max()]
|
|
# 1. Encoder
|
if hasattr(self.encoder, "overlap_chunk_cls"):
|
ind = self.encoder.overlap_chunk_cls.random_choice(self.training, decoding_ind)
|
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths, ind=ind)
|
else:
|
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, pre_loss_att, acc_att, cer_att, wer_att = None, None, None, None, None
|
loss_ctc, cer_ctc = None, None
|
loss_pre = 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
|
|
# 2b. Attention decoder branch
|
if self.ctc_weight != 1.0:
|
loss_att, acc_att, cer_att, wer_att, loss_pre, pre_loss_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 + loss_pre * self.predictor_weight
|
elif self.ctc_weight == 1.0:
|
loss = loss_ctc
|
else:
|
loss = self.ctc_weight * loss_ctc + (1 - self.ctc_weight) * loss_att + loss_pre * self.predictor_weight
|
|
if self.use_1st_decoder_loss and pre_loss_att is not None:
|
loss = loss + (1 - self.ctc_weight) * pre_loss_att
|
|
# Collect Attn branch stats
|
stats["loss_att"] = loss_att.detach() if loss_att is not None else None
|
stats["pre_loss_att"] = pre_loss_att.detach() if pre_loss_att is not None else None
|
stats["acc"] = acc_att
|
stats["cer"] = cer_att
|
stats["wer"] = wer_att
|
stats["loss_pre"] = loss_pre.detach().cpu() if loss_pre is not None else None
|
|
stats["loss"] = torch.clone(loss.detach())
|
|
# force_gatherable: to-device and to-tensor if scalar for DataParallel
|
if self.length_normalized_loss:
|
batch_size = (text_lengths + self.predictor_bias).sum()
|
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, ind: int = 0,
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
"""Frontend + Encoder. Note that this method is used by asr_inference.py
|
Args:
|
speech: (Batch, Length, ...)
|
speech_lengths: (Batch, )
|
ind: int
|
"""
|
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(speech, speech_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)
|
if self.encoder.interctc_use_conditioning:
|
if hasattr(self.encoder, "overlap_chunk_cls"):
|
encoder_out, encoder_out_lens, _ = self.encoder(
|
feats, feats_lengths, ctc=self.ctc, ind=ind
|
)
|
encoder_out, encoder_out_lens = self.encoder.overlap_chunk_cls.remove_chunk(encoder_out,
|
encoder_out_lens,
|
chunk_outs=None)
|
else:
|
encoder_out, encoder_out_lens, _ = self.encoder(
|
feats, feats_lengths, ctc=self.ctc
|
)
|
else:
|
if hasattr(self.encoder, "overlap_chunk_cls"):
|
encoder_out, encoder_out_lens, _ = self.encoder(feats, feats_lengths, ind=ind)
|
encoder_out, encoder_out_lens = self.encoder.overlap_chunk_cls.remove_chunk(encoder_out,
|
encoder_out_lens,
|
chunk_outs=None)
|
else:
|
encoder_out, encoder_out_lens, _ = self.encoder(feats, feats_lengths)
|
intermediate_outs = None
|
if isinstance(encoder_out, tuple):
|
intermediate_outs = encoder_out[1]
|
encoder_out = encoder_out[0]
|
|
# # Post-encoder, e.g. NLU
|
# if self.postencoder is not None:
|
# encoder_out, encoder_out_lens = self.postencoder(
|
# encoder_out, encoder_out_lens
|
# )
|
|
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(),
|
)
|
|
if intermediate_outs is not None:
|
return (encoder_out, intermediate_outs), encoder_out_lens
|
|
return encoder_out, encoder_out_lens
|
|
def calc_predictor(self, encoder_out, encoder_out_lens):
|
|
encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
|
encoder_out.device)
|
pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = self.predictor(encoder_out, None, encoder_out_mask,
|
ignore_id=self.ignore_id)
|
return pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index
|
|
def cal_decoder_with_predictor(self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens):
|
|
decoder_outs = self.decoder(
|
encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens
|
)
|
decoder_out = decoder_outs[0]
|
decoder_out = torch.log_softmax(decoder_out, dim=-1)
|
return decoder_out, ys_pad_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 nll(
|
self,
|
encoder_out: torch.Tensor,
|
encoder_out_lens: torch.Tensor,
|
ys_pad: torch.Tensor,
|
ys_pad_lens: torch.Tensor,
|
) -> torch.Tensor:
|
"""Compute negative log likelihood(nll) from transformer-decoder
|
Normally, this function is called in batchify_nll.
|
Args:
|
encoder_out: (Batch, Length, Dim)
|
encoder_out_lens: (Batch,)
|
ys_pad: (Batch, Length)
|
ys_pad_lens: (Batch,)
|
"""
|
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
|
) # [batch, seqlen, dim]
|
batch_size = decoder_out.size(0)
|
decoder_num_class = decoder_out.size(2)
|
# nll: negative log-likelihood
|
nll = torch.nn.functional.cross_entropy(
|
decoder_out.view(-1, decoder_num_class),
|
ys_out_pad.view(-1),
|
ignore_index=self.ignore_id,
|
reduction="none",
|
)
|
nll = nll.view(batch_size, -1)
|
nll = nll.sum(dim=1)
|
assert nll.size(0) == batch_size
|
return nll
|
|
def batchify_nll(
|
self,
|
encoder_out: torch.Tensor,
|
encoder_out_lens: torch.Tensor,
|
ys_pad: torch.Tensor,
|
ys_pad_lens: torch.Tensor,
|
batch_size: int = 100,
|
):
|
"""Compute negative log likelihood(nll) from transformer-decoder
|
To avoid OOM, this fuction seperate the input into batches.
|
Then call nll for each batch and combine and return results.
|
Args:
|
encoder_out: (Batch, Length, Dim)
|
encoder_out_lens: (Batch,)
|
ys_pad: (Batch, Length)
|
ys_pad_lens: (Batch,)
|
batch_size: int, samples each batch contain when computing nll,
|
you may change this to avoid OOM or increase
|
GPU memory usage
|
"""
|
total_num = encoder_out.size(0)
|
if total_num <= batch_size:
|
nll = self.nll(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens)
|
else:
|
nll = []
|
start_idx = 0
|
while True:
|
end_idx = min(start_idx + batch_size, total_num)
|
batch_encoder_out = encoder_out[start_idx:end_idx, :, :]
|
batch_encoder_out_lens = encoder_out_lens[start_idx:end_idx]
|
batch_ys_pad = ys_pad[start_idx:end_idx, :]
|
batch_ys_pad_lens = ys_pad_lens[start_idx:end_idx]
|
batch_nll = self.nll(
|
batch_encoder_out,
|
batch_encoder_out_lens,
|
batch_ys_pad,
|
batch_ys_pad_lens,
|
)
|
nll.append(batch_nll)
|
start_idx = end_idx
|
if start_idx == total_num:
|
break
|
nll = torch.cat(nll)
|
assert nll.size(0) == total_num
|
return nll
|
|
def _calc_att_loss(
|
self,
|
encoder_out: torch.Tensor,
|
encoder_out_lens: torch.Tensor,
|
ys_pad: torch.Tensor,
|
ys_pad_lens: torch.Tensor,
|
):
|
encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
|
encoder_out.device)
|
if self.predictor_bias == 1:
|
_, ys_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
|
ys_pad_lens = ys_pad_lens + self.predictor_bias
|
pre_acoustic_embeds, pre_token_length, _, pre_peak_index = self.predictor(encoder_out, ys_pad, encoder_out_mask,
|
ignore_id=self.ignore_id)
|
|
# 0. sampler
|
decoder_out_1st = None
|
pre_loss_att = None
|
if self.sampling_ratio > 0.0:
|
|
|
if self.use_1st_decoder_loss:
|
sematic_embeds, decoder_out_1st, pre_loss_att = self.sampler_with_grad(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens,
|
pre_acoustic_embeds)
|
else:
|
sematic_embeds, decoder_out_1st = self.sampler(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens,
|
pre_acoustic_embeds)
|
else:
|
if self.step_cur < 2:
|
logging.info("disable sampler in paraformer, sampling_ratio: {}".format(self.sampling_ratio))
|
sematic_embeds = pre_acoustic_embeds
|
|
# 1. Forward decoder
|
decoder_outs = self.decoder(
|
encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens
|
)
|
decoder_out, _ = decoder_outs[0], decoder_outs[1]
|
|
if decoder_out_1st is None:
|
decoder_out_1st = decoder_out
|
# 2. Compute attention loss
|
loss_att = self.criterion_att(decoder_out, ys_pad)
|
acc_att = th_accuracy(
|
decoder_out_1st.view(-1, self.vocab_size),
|
ys_pad,
|
ignore_label=self.ignore_id,
|
)
|
loss_pre = self.criterion_pre(ys_pad_lens.type_as(pre_token_length), pre_token_length)
|
|
# 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_1st.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, loss_pre, pre_loss_att
|
|
def sampler(self, encoder_out, encoder_out_lens, ys_pad, ys_pad_lens, pre_acoustic_embeds):
|
|
tgt_mask = (~make_pad_mask(ys_pad_lens, maxlen=ys_pad_lens.max())[:, :, None]).to(ys_pad.device)
|
ys_pad_masked = ys_pad * tgt_mask[:, :, 0]
|
if self.share_embedding:
|
ys_pad_embed = self.decoder.output_layer.weight[ys_pad_masked]
|
else:
|
ys_pad_embed = self.decoder.embed(ys_pad_masked)
|
with torch.no_grad():
|
decoder_outs = self.decoder(
|
encoder_out, encoder_out_lens, pre_acoustic_embeds, ys_pad_lens
|
)
|
decoder_out, _ = decoder_outs[0], decoder_outs[1]
|
pred_tokens = decoder_out.argmax(-1)
|
nonpad_positions = ys_pad.ne(self.ignore_id)
|
seq_lens = (nonpad_positions).sum(1)
|
same_num = ((pred_tokens == ys_pad) & nonpad_positions).sum(1)
|
input_mask = torch.ones_like(nonpad_positions)
|
bsz, seq_len = ys_pad.size()
|
for li in range(bsz):
|
target_num = (((seq_lens[li] - same_num[li].sum()).float()) * self.sampling_ratio).long()
|
if target_num > 0:
|
input_mask[li].scatter_(dim=0, index=torch.randperm(seq_lens[li])[:target_num].to(input_mask.device), value=0)
|
input_mask = input_mask.eq(1)
|
input_mask = input_mask.masked_fill(~nonpad_positions, False)
|
input_mask_expand_dim = input_mask.unsqueeze(2).to(pre_acoustic_embeds.device)
|
|
sematic_embeds = pre_acoustic_embeds.masked_fill(~input_mask_expand_dim, 0) + ys_pad_embed.masked_fill(
|
input_mask_expand_dim, 0)
|
return sematic_embeds * tgt_mask, decoder_out * tgt_mask
|
|
def sampler_with_grad(self, encoder_out, encoder_out_lens, ys_pad, ys_pad_lens, pre_acoustic_embeds):
|
tgt_mask = (~make_pad_mask(ys_pad_lens, maxlen=ys_pad_lens.max())[:, :, None]).to(ys_pad.device)
|
ys_pad_masked = ys_pad * tgt_mask[:, :, 0]
|
if self.share_embedding:
|
ys_pad_embed = self.decoder.output_layer.weight[ys_pad_masked]
|
else:
|
ys_pad_embed = self.decoder.embed(ys_pad_masked)
|
decoder_outs = self.decoder(
|
encoder_out, encoder_out_lens, pre_acoustic_embeds, ys_pad_lens
|
)
|
pre_loss_att = self.criterion_att(decoder_outs[0], ys_pad)
|
decoder_out, _ = decoder_outs[0], decoder_outs[1]
|
pred_tokens = decoder_out.argmax(-1)
|
nonpad_positions = ys_pad.ne(self.ignore_id)
|
seq_lens = (nonpad_positions).sum(1)
|
same_num = ((pred_tokens == ys_pad) & nonpad_positions).sum(1)
|
input_mask = torch.ones_like(nonpad_positions)
|
bsz, seq_len = ys_pad.size()
|
for li in range(bsz):
|
target_num = (((seq_lens[li] - same_num[li].sum()).float()) * self.sampling_ratio).long()
|
if target_num > 0:
|
input_mask[li].scatter_(dim=0, index=torch.randperm(seq_lens[li])[:target_num].to(input_mask.device), value=0)
|
input_mask = input_mask.eq(1)
|
input_mask = input_mask.masked_fill(~nonpad_positions, False)
|
input_mask_expand_dim = input_mask.unsqueeze(2).to(pre_acoustic_embeds.device)
|
|
sematic_embeds = pre_acoustic_embeds.masked_fill(~input_mask_expand_dim, 0) + ys_pad_embed.masked_fill(
|
input_mask_expand_dim, 0)
|
|
return sematic_embeds * tgt_mask, decoder_out * tgt_mask, pre_loss_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
|
