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 random
|
import numpy as np
|
from typeguard import check_argument_types
|
|
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.train.abs_espnet_model import AbsESPnetModel
|
from funasr.models.predictor.cif import CifPredictorV3
|
|
|
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(AbsESPnetModel):
|
"""
|
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,
|
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],
|
decoder: AbsDecoder,
|
ctc: CTC,
|
ctc_weight: float = 0.5,
|
interctc_weight: float = 0.0,
|
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,
|
):
|
assert check_argument_types()
|
assert 0.0 <= ctc_weight <= 1.0, ctc_weight
|
assert 0.0 <= interctc_weight < 1.0, interctc_weight
|
|
super().__init__()
|
# note that eos is the same as sos (equivalent ID)
|
self.blank_id = blank_id
|
self.sos = vocab_size - 1 if sos is None else sos
|
self.eos = vocab_size - 1 if eos is None else eos
|
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
|
|
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]
|
self.step_cur += 1
|
# for data-parallel
|
text = text[:, : text_lengths.max()]
|
speech = speech[:, :speech_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_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 = 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
|
|
# 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
|
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
|
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)
|
# -> encoder_out: (Batch, Length2, Dim2)
|
if self.encoder.interctc_use_conditioning:
|
encoder_out, encoder_out_lens, _ = self.encoder(
|
feats, feats_lengths, ctc=self.ctc
|
)
|
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
|
if self.sampling_ratio > 0.0:
|
if self.step_cur < 2:
|
logging.info("enable sampler in paraformer, sampling_ratio: {}".format(self.sampling_ratio))
|
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
|
|
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].cuda(), 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 _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
|
|
|
class ParaformerOnline(Paraformer):
|
"""
|
Author: Speech Lab, Alibaba Group, China
|
Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
|
https://arxiv.org/abs/2206.08317
|
"""
|
|
def __init__(
|
self, *args, **kwargs,
|
):
|
super().__init__(*args, **kwargs)
|
|
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]
|
self.step_cur += 1
|
# for data-parallel
|
text = text[:, : text_lengths.max()]
|
speech = speech[:, :speech_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_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 = 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
|
|
# 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
|
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
|
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
|
return loss, stats, weight
|
|
def encode_chunk(
|
self, speech: torch.Tensor, speech_lengths: torch.Tensor, cache: dict = None
|
) -> 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)
|
if self.encoder.interctc_use_conditioning:
|
encoder_out, encoder_out_lens, _ = self.encoder.forward_chunk(
|
feats, feats_lengths, cache=cache["encoder"], ctc=self.ctc
|
)
|
else:
|
encoder_out, encoder_out_lens, _ = self.encoder.forward_chunk(feats, feats_lengths, cache=cache["encoder"])
|
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
|
)
|
|
if intermediate_outs is not None:
|
return (encoder_out, intermediate_outs), encoder_out_lens
|
|
return encoder_out, torch.tensor([encoder_out.size(1)])
|
|
def calc_predictor_chunk(self, encoder_out, cache=None):
|
|
pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = \
|
self.predictor.forward_chunk(encoder_out, cache["encoder"])
|
return pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index
|
|
def cal_decoder_with_predictor_chunk(self, encoder_out, sematic_embeds, cache=None):
|
decoder_outs = self.decoder.forward_chunk(
|
encoder_out, sematic_embeds, cache["decoder"]
|
)
|
decoder_out = decoder_outs
|
decoder_out = torch.log_softmax(decoder_out, dim=-1)
|
return decoder_out
|
|
|
class ParaformerBert(Paraformer):
|
"""
|
Author: Speech Lab of DAMO Academy, Alibaba Group
|
Paraformer2: advanced paraformer with LFMMI and bert for non-autoregressive end-to-end speech recognition
|
"""
|
|
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],
|
decoder: AbsDecoder,
|
ctc: CTC,
|
ctc_weight: float = 0.5,
|
interctc_weight: float = 0.0,
|
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,
|
embeds_id: int = 2,
|
embeds_loss_weight: float = 0.0,
|
embed_dims: int = 768,
|
):
|
assert check_argument_types()
|
assert 0.0 <= ctc_weight <= 1.0, ctc_weight
|
assert 0.0 <= interctc_weight < 1.0, interctc_weight
|
|
super().__init__(
|
vocab_size=vocab_size,
|
token_list=token_list,
|
frontend=frontend,
|
specaug=specaug,
|
normalize=normalize,
|
preencoder=preencoder,
|
encoder=encoder,
|
postencoder=postencoder,
|
decoder=decoder,
|
ctc=ctc,
|
ctc_weight=ctc_weight,
|
interctc_weight=interctc_weight,
|
ignore_id=ignore_id,
|
blank_id=blank_id,
|
sos=sos,
|
eos=eos,
|
lsm_weight=lsm_weight,
|
length_normalized_loss=length_normalized_loss,
|
report_cer=report_cer,
|
report_wer=report_wer,
|
sym_space=sym_space,
|
sym_blank=sym_blank,
|
extract_feats_in_collect_stats=extract_feats_in_collect_stats,
|
predictor=predictor,
|
predictor_weight=predictor_weight,
|
predictor_bias=predictor_bias,
|
sampling_ratio=sampling_ratio,
|
)
|
self.decoder.embeds_id = embeds_id
|
decoder_attention_dim = self.decoder.attention_dim
|
self.pro_nn = torch.nn.Linear(decoder_attention_dim, embed_dims)
|
self.cos = torch.nn.CosineSimilarity(dim=-1, eps=1e-6)
|
self.embeds_loss_weight = embeds_loss_weight
|
self.length_normalized_loss = length_normalized_loss
|
|
def _calc_embed_loss(self,
|
ys_pad: torch.Tensor,
|
ys_pad_lens: torch.Tensor,
|
embed: torch.Tensor = None,
|
embed_lengths: torch.Tensor = None,
|
embeds_outputs: torch.Tensor = None,
|
):
|
embeds_outputs = self.pro_nn(embeds_outputs)
|
tgt_mask = (~make_pad_mask(ys_pad_lens, maxlen=ys_pad_lens.max())[:, :, None]).to(ys_pad.device)
|
embeds_outputs *= tgt_mask # b x l x d
|
embed *= tgt_mask # b x l x d
|
cos_loss = 1.0 - self.cos(embeds_outputs, embed)
|
cos_loss *= tgt_mask.squeeze(2)
|
if self.length_normalized_loss:
|
token_num_total = torch.sum(tgt_mask)
|
else:
|
token_num_total = tgt_mask.size()[0]
|
cos_loss_total = torch.sum(cos_loss)
|
cos_loss = cos_loss_total / token_num_total
|
# print("cos_loss: {}".format(cos_loss))
|
return cos_loss
|
|
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
|
if self.sampling_ratio > 0.0:
|
if self.step_cur < 2:
|
logging.info(
|
"enable sampler in paraformer, sampling_ratio: {}".format(self.sampling_ratio))
|
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]
|
embeds_outputs = None
|
if len(decoder_outs) > 2:
|
embeds_outputs = decoder_outs[2]
|
|
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, embeds_outputs
|
|
def forward(
|
self,
|
speech: torch.Tensor,
|
speech_lengths: torch.Tensor,
|
text: torch.Tensor,
|
text_lengths: torch.Tensor,
|
embed: torch.Tensor = None,
|
embed_lengths: torch.Tensor = None,
|
) -> 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]
|
self.step_cur += 1
|
# for data-parallel
|
text = text[:, : text_lengths.max()]
|
speech = speech[:, :speech_lengths.max(), :]
|
if embed is not None:
|
embed = embed[:, :embed_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_pre = 0.0
|
cos_loss = 0.0
|
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_ret = self._calc_att_loss(
|
encoder_out, encoder_out_lens, text, text_lengths
|
)
|
loss_att, acc_att, cer_att, wer_att, loss_pre = loss_ret[0], loss_ret[1], loss_ret[2], loss_ret[3], \
|
loss_ret[4]
|
embeds_outputs = None
|
if len(loss_ret) > 5:
|
embeds_outputs = loss_ret[5]
|
if embeds_outputs is not None:
|
cos_loss = self._calc_embed_loss(text, text_lengths, embed, embed_lengths, embeds_outputs)
|
|
# 3. CTC-Att loss definition
|
if self.ctc_weight == 0.0:
|
loss = loss_att + loss_pre * self.predictor_weight + cos_loss * self.embeds_loss_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 + cos_loss * self.embeds_loss_weight
|
|
# 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
|
stats["loss_pre"] = loss_pre.detach().cpu() if loss_pre > 0.0 else None
|
stats["cos_loss"] = cos_loss.detach().cpu() if cos_loss > 0.0 else None
|
|
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
|
|
|
class BiCifParaformer(Paraformer):
|
|
"""
|
Paraformer model with an extra cif predictor
|
to conduct accurate timestamp prediction
|
"""
|
|
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],
|
decoder: AbsDecoder,
|
ctc: CTC,
|
ctc_weight: float = 0.5,
|
interctc_weight: float = 0.0,
|
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,
|
):
|
assert check_argument_types()
|
assert 0.0 <= ctc_weight <= 1.0, ctc_weight
|
assert 0.0 <= interctc_weight < 1.0, interctc_weight
|
|
super().__init__(
|
vocab_size=vocab_size,
|
token_list=token_list,
|
frontend=frontend,
|
specaug=specaug,
|
normalize=normalize,
|
preencoder=preencoder,
|
encoder=encoder,
|
postencoder=postencoder,
|
decoder=decoder,
|
ctc=ctc,
|
ctc_weight=ctc_weight,
|
interctc_weight=interctc_weight,
|
ignore_id=ignore_id,
|
blank_id=blank_id,
|
sos=sos,
|
eos=eos,
|
lsm_weight=lsm_weight,
|
length_normalized_loss=length_normalized_loss,
|
report_cer=report_cer,
|
report_wer=report_wer,
|
sym_space=sym_space,
|
sym_blank=sym_blank,
|
extract_feats_in_collect_stats=extract_feats_in_collect_stats,
|
predictor=predictor,
|
predictor_weight=predictor_weight,
|
predictor_bias=predictor_bias,
|
sampling_ratio=sampling_ratio,
|
)
|
assert isinstance(self.predictor, CifPredictorV3), "BiCifParaformer should use CIFPredictorV3"
|
|
def _calc_pre2_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_token_length2 = self.predictor(encoder_out, ys_pad, encoder_out_mask, ignore_id=self.ignore_id)
|
|
# loss_pre = self.criterion_pre(ys_pad_lens.type_as(pre_token_length), pre_token_length)
|
loss_pre2 = self.criterion_pre(ys_pad_lens.type_as(pre_token_length2), pre_token_length2)
|
|
return loss_pre2
|
|
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
|
if self.sampling_ratio > 0.0:
|
if self.step_cur < 2:
|
logging.info("enable sampler in paraformer, sampling_ratio: {}".format(self.sampling_ratio))
|
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
|
|
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, pre_token_length2 = 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 calc_predictor_timestamp(self, encoder_out, encoder_out_lens, token_num):
|
encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
|
encoder_out.device)
|
ds_alphas, ds_cif_peak, us_alphas, us_peaks = self.predictor.get_upsample_timestamp(encoder_out,
|
encoder_out_mask,
|
token_num)
|
return ds_alphas, ds_cif_peak, us_alphas, us_peaks
|
|
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]
|
self.step_cur += 1
|
# for data-parallel
|
text = text[:, : text_lengths.max()]
|
speech = speech[:, :speech_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_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 = self._calc_att_loss(
|
encoder_out, encoder_out_lens, text, text_lengths
|
)
|
|
loss_pre2 = self._calc_pre2_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 + loss_pre2 * self.predictor_weight * 0.5
|
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 + loss_pre2 * self.predictor_weight * 0.5
|
|
# 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
|
stats["loss_pre"] = loss_pre.detach().cpu() if loss_pre is not None else None
|
stats["loss_pre2"] = loss_pre2.detach().cpu()
|
|
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
|
|
|
class ContextualParaformer(Paraformer):
|
"""
|
Paraformer model with contextual hotword
|
"""
|
|
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],
|
decoder: AbsDecoder,
|
ctc: CTC,
|
ctc_weight: float = 0.5,
|
interctc_weight: float = 0.0,
|
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,
|
min_hw_length: int = 2,
|
max_hw_length: int = 4,
|
sample_rate: float = 0.6,
|
batch_rate: float = 0.5,
|
double_rate: float = -1.0,
|
target_buffer_length: int = -1,
|
inner_dim: int = 256,
|
bias_encoder_type: str = 'lstm',
|
label_bracket: bool = False,
|
use_decoder_embedding: bool = False,
|
):
|
assert check_argument_types()
|
assert 0.0 <= ctc_weight <= 1.0, ctc_weight
|
assert 0.0 <= interctc_weight < 1.0, interctc_weight
|
|
super().__init__(
|
vocab_size=vocab_size,
|
token_list=token_list,
|
frontend=frontend,
|
specaug=specaug,
|
normalize=normalize,
|
preencoder=preencoder,
|
encoder=encoder,
|
postencoder=postencoder,
|
decoder=decoder,
|
ctc=ctc,
|
ctc_weight=ctc_weight,
|
interctc_weight=interctc_weight,
|
ignore_id=ignore_id,
|
blank_id=blank_id,
|
sos=sos,
|
eos=eos,
|
lsm_weight=lsm_weight,
|
length_normalized_loss=length_normalized_loss,
|
report_cer=report_cer,
|
report_wer=report_wer,
|
sym_space=sym_space,
|
sym_blank=sym_blank,
|
extract_feats_in_collect_stats=extract_feats_in_collect_stats,
|
predictor=predictor,
|
predictor_weight=predictor_weight,
|
predictor_bias=predictor_bias,
|
sampling_ratio=sampling_ratio,
|
)
|
|
if bias_encoder_type == 'lstm':
|
logging.warning("enable bias encoder sampling and contextual training")
|
self.bias_encoder = torch.nn.LSTM(inner_dim, inner_dim, 1, batch_first=True, dropout=0)
|
self.bias_embed = torch.nn.Embedding(vocab_size, inner_dim)
|
else:
|
logging.error("Unsupport bias encoder type")
|
|
self.min_hw_length = min_hw_length
|
self.max_hw_length = max_hw_length
|
self.sample_rate = sample_rate
|
self.batch_rate = batch_rate
|
self.target_buffer_length = target_buffer_length
|
self.double_rate = double_rate
|
|
if self.target_buffer_length > 0:
|
self.hotword_buffer = None
|
self.length_record = []
|
self.current_buffer_length = 0
|
self.use_decoder_embedding = use_decoder_embedding
|
|
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]
|
self.step_cur += 1
|
# for data-parallel
|
text = text[:, : text_lengths.max()]
|
speech = speech[:, :speech_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_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 = 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
|
|
# 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
|
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
|
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
|
return loss, stats, weight
|
|
def _sample_hot_word(self, ys_pad, ys_pad_lens):
|
hw_list = [torch.Tensor([0]).long().to(ys_pad.device)]
|
hw_lengths = [0] # this length is actually for indice, so -1
|
for i, length in enumerate(ys_pad_lens):
|
if length < 2:
|
continue
|
if length > self.min_hw_length + self.max_hw_length + 2 and random.random() < self.double_rate:
|
# sample double hotword
|
_max_hw_length = min(self.max_hw_length, length // 2)
|
# first hotword
|
start1 = random.randint(0, length // 3)
|
end1 = random.randint(start1 + self.min_hw_length - 1, start1 + _max_hw_length - 1)
|
hw_tokens1 = ys_pad[i][start1:end1 + 1]
|
hw_lengths.append(len(hw_tokens1) - 1)
|
hw_list.append(hw_tokens1)
|
# second hotword
|
start2 = random.randint(end1 + 1, length - self.min_hw_length)
|
end2 = random.randint(min(length - 1, start2 + self.min_hw_length - 1),
|
min(length - 1, start2 + self.max_hw_length - 1))
|
hw_tokens2 = ys_pad[i][start2:end2 + 1]
|
hw_lengths.append(len(hw_tokens2) - 1)
|
hw_list.append(hw_tokens2)
|
continue
|
if random.random() < self.sample_rate:
|
if length == 2:
|
hw_tokens = ys_pad[i][:2]
|
hw_lengths.append(1)
|
hw_list.append(hw_tokens)
|
else:
|
start = random.randint(0, length - self.min_hw_length)
|
end = random.randint(min(length - 1, start + self.min_hw_length - 1),
|
min(length - 1, start + self.max_hw_length - 1)) + 1
|
# print(start, end)
|
hw_tokens = ys_pad[i][start:end]
|
hw_lengths.append(len(hw_tokens) - 1)
|
hw_list.append(hw_tokens)
|
# padding
|
hw_list_pad = pad_list(hw_list, 0)
|
if self.use_decoder_embedding:
|
hw_embed = self.decoder.embed(hw_list_pad)
|
else:
|
hw_embed = self.bias_embed(hw_list_pad)
|
hw_embed, (_, _) = self.bias_encoder(hw_embed)
|
_ind = np.arange(0, len(hw_list)).tolist()
|
# update self.hotword_buffer, throw a part if oversize
|
selected = hw_embed[_ind, hw_lengths]
|
if self.target_buffer_length > 0:
|
_b = selected.shape[0]
|
if self.hotword_buffer is None:
|
self.hotword_buffer = selected
|
self.length_record.append(selected.shape[0])
|
self.current_buffer_length = _b
|
elif self.current_buffer_length + _b < self.target_buffer_length:
|
self.hotword_buffer = torch.cat([self.hotword_buffer.detach(), selected], dim=0)
|
self.current_buffer_length += _b
|
selected = self.hotword_buffer
|
else:
|
self.hotword_buffer = torch.cat([self.hotword_buffer.detach(), selected], dim=0)
|
random_throw = random.randint(self.target_buffer_length // 2, self.target_buffer_length) + 10
|
self.hotword_buffer = self.hotword_buffer[-1 * random_throw:]
|
selected = self.hotword_buffer
|
self.current_buffer_length = selected.shape[0]
|
return selected.squeeze(0).repeat(ys_pad.shape[0], 1, 1).to(ys_pad.device)
|
|
def sampler(self, encoder_out, encoder_out_lens, ys_pad, ys_pad_lens, pre_acoustic_embeds, contextual_info):
|
|
tgt_mask = (~make_pad_mask(ys_pad_lens, maxlen=ys_pad_lens.max())[:, :, None]).to(ys_pad.device)
|
ys_pad = ys_pad * tgt_mask[:, :, 0]
|
if self.share_embedding:
|
ys_pad_embed = self.decoder.output_layer.weight[ys_pad]
|
else:
|
ys_pad_embed = self.decoder.embed(ys_pad)
|
with torch.no_grad():
|
decoder_outs = self.decoder(
|
encoder_out, encoder_out_lens, pre_acoustic_embeds, ys_pad_lens, contextual_info=contextual_info
|
)
|
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].cuda(), 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 _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)
|
|
# sample hot word
|
contextual_info = self._sample_hot_word(ys_pad, ys_pad_lens)
|
|
# 0. sampler
|
decoder_out_1st = None
|
if self.sampling_ratio > 0.0:
|
if self.step_cur < 2:
|
logging.info("enable sampler in paraformer, sampling_ratio: {}".format(self.sampling_ratio))
|
sematic_embeds, decoder_out_1st = self.sampler(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens,
|
pre_acoustic_embeds, contextual_info)
|
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, contextual_info=contextual_info
|
)
|
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
|
|
def cal_decoder_with_predictor(self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, hw_list=None):
|
if hw_list is None:
|
# default hotword list
|
hw_list = [torch.Tensor([self.sos]).long().to(encoder_out.device)] # empty hotword list
|
hw_list_pad = pad_list(hw_list, 0)
|
if self.use_decoder_embedding:
|
hw_embed = self.decoder.embed(hw_list_pad)
|
else:
|
hw_embed = self.bias_embed(hw_list_pad)
|
_, (h_n, _) = self.bias_encoder(hw_embed)
|
contextual_info = h_n.squeeze(0).repeat(encoder_out.shape[0], 1, 1)
|
else:
|
hw_lengths = [len(i) for i in hw_list]
|
hw_list_pad = pad_list([torch.Tensor(i).long() for i in hw_list], 0).to(encoder_out.device)
|
if self.use_decoder_embedding:
|
hw_embed = self.decoder.embed(hw_list_pad)
|
else:
|
hw_embed = self.bias_embed(hw_list_pad)
|
hw_embed = torch.nn.utils.rnn.pack_padded_sequence(hw_embed, hw_lengths, batch_first=True,
|
enforce_sorted=False)
|
_, (h_n, _) = self.bias_encoder(hw_embed)
|
# hw_embed, _ = torch.nn.utils.rnn.pad_packed_sequence(hw_embed, batch_first=True)
|
contextual_info = h_n.squeeze(0).repeat(encoder_out.shape[0], 1, 1)
|
decoder_outs = self.decoder(
|
encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, contextual_info=contextual_info
|
)
|
decoder_out = decoder_outs[0]
|
decoder_out = torch.log_softmax(decoder_out, dim=-1)
|
return decoder_out, ys_pad_lens
|
|
def gen_clas_tf2torch_map_dict(self):
|
tensor_name_prefix_torch = "bias_encoder"
|
tensor_name_prefix_tf = "seq2seq/clas_charrnn"
|
|
tensor_name_prefix_torch_emb = "bias_embed"
|
tensor_name_prefix_tf_emb = "seq2seq"
|
|
map_dict_local = {
|
# in lstm
|
"{}.weight_ih_l0".format(tensor_name_prefix_torch):
|
{"name": "{}/rnn/lstm_cell/kernel".format(tensor_name_prefix_tf),
|
"squeeze": None,
|
"transpose": (1, 0),
|
"slice": (0, 512),
|
"unit_k": 512,
|
}, # (1024, 2048),(2048,512)
|
"{}.weight_hh_l0".format(tensor_name_prefix_torch):
|
{"name": "{}/rnn/lstm_cell/kernel".format(tensor_name_prefix_tf),
|
"squeeze": None,
|
"transpose": (1, 0),
|
"slice": (512, 1024),
|
"unit_k": 512,
|
}, # (1024, 2048),(2048,512)
|
"{}.bias_ih_l0".format(tensor_name_prefix_torch):
|
{"name": "{}/rnn/lstm_cell/bias".format(tensor_name_prefix_tf),
|
"squeeze": None,
|
"transpose": None,
|
"scale": 0.5,
|
"unit_b": 512,
|
}, # (2048,),(2048,)
|
"{}.bias_hh_l0".format(tensor_name_prefix_torch):
|
{"name": "{}/rnn/lstm_cell/bias".format(tensor_name_prefix_tf),
|
"squeeze": None,
|
"transpose": None,
|
"scale": 0.5,
|
"unit_b": 512,
|
}, # (2048,),(2048,)
|
|
# in embed
|
"{}.weight".format(tensor_name_prefix_torch_emb):
|
{"name": "{}/contextual_encoder/w_char_embs".format(tensor_name_prefix_tf_emb),
|
"squeeze": None,
|
"transpose": None,
|
}, # (4235,256),(4235,256)
|
}
|
return map_dict_local
|
|
def clas_convert_tf2torch(self,
|
var_dict_tf,
|
var_dict_torch):
|
map_dict = self.gen_clas_tf2torch_map_dict()
|
var_dict_torch_update = dict()
|
for name in sorted(var_dict_torch.keys(), reverse=False):
|
names = name.split('.')
|
if names[0] == "bias_encoder":
|
name_q = name
|
if name_q in map_dict.keys():
|
name_v = map_dict[name_q]["name"]
|
name_tf = name_v
|
data_tf = var_dict_tf[name_tf]
|
if map_dict[name_q].get("unit_k") is not None:
|
dim = map_dict[name_q]["unit_k"]
|
i = data_tf[:, 0:dim].copy()
|
f = data_tf[:, dim:2 * dim].copy()
|
o = data_tf[:, 2 * dim:3 * dim].copy()
|
g = data_tf[:, 3 * dim:4 * dim].copy()
|
data_tf = np.concatenate([i, o, f, g], axis=1)
|
if map_dict[name_q].get("unit_b") is not None:
|
dim = map_dict[name_q]["unit_b"]
|
i = data_tf[0:dim].copy()
|
f = data_tf[dim:2 * dim].copy()
|
o = data_tf[2 * dim:3 * dim].copy()
|
g = data_tf[3 * dim:4 * dim].copy()
|
data_tf = np.concatenate([i, o, f, g], axis=0)
|
if map_dict[name_q]["squeeze"] is not None:
|
data_tf = np.squeeze(data_tf, axis=map_dict[name_q]["squeeze"])
|
if map_dict[name_q].get("slice") is not None:
|
data_tf = data_tf[map_dict[name_q]["slice"][0]:map_dict[name_q]["slice"][1]]
|
if map_dict[name_q].get("scale") is not None:
|
data_tf = data_tf * map_dict[name_q]["scale"]
|
if map_dict[name_q]["transpose"] is not None:
|
data_tf = np.transpose(data_tf, map_dict[name_q]["transpose"])
|
data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
|
assert var_dict_torch[name].size() == data_tf.size(), "{}, {}, {} != {}".format(name, name_tf,
|
var_dict_torch[
|
name].size(),
|
data_tf.size())
|
var_dict_torch_update[name] = data_tf
|
logging.info(
|
"torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_v,
|
var_dict_tf[name_tf].shape))
|
elif names[0] == "bias_embed":
|
name_tf = map_dict[name]["name"]
|
data_tf = var_dict_tf[name_tf]
|
if map_dict[name]["squeeze"] is not None:
|
data_tf = np.squeeze(data_tf, axis=map_dict[name]["squeeze"])
|
if map_dict[name]["transpose"] is not None:
|
data_tf = np.transpose(data_tf, map_dict[name]["transpose"])
|
data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
|
assert var_dict_torch[name].size() == data_tf.size(), "{}, {}, {} != {}".format(name, name_tf,
|
var_dict_torch[
|
name].size(),
|
data_tf.size())
|
var_dict_torch_update[name] = data_tf
|
logging.info(
|
"torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_tf,
|
var_dict_tf[name_tf].shape))
|
|
return var_dict_torch_update
|
