python/FunASR-XL.git

			@@ -12,24 +12,20 @@
			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.models.base_model import FunASRModel
			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


			@@ -42,7 +38,7 @@
			yield


			class Paraformer(AbsESPnetModel):
			class Paraformer(FunASRModel):
			"""
			Author: Speech Lab, Alibaba Group, China
			Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
			@@ -53,11 +49,11 @@
			self,
			vocab_size: int,
			token_list: Union[Tuple[str, ...], List[str]],
			frontend: Optional[AbsFrontend],
			specaug: Optional[AbsSpecAug],
			normalize: Optional[AbsNormalize],
			frontend: Optional[torch.nn.Module],
			specaug: Optional[torch.nn.Module],
			normalize: Optional[torch.nn.Module],
			preencoder: Optional[AbsPreEncoder],
			encoder: AbsEncoder,
			encoder: torch.nn.Module,
			postencoder: Optional[AbsPostEncoder],
			decoder: AbsDecoder,
			ctc: CTC,
			@@ -325,12 +321,67 @@

			return encoder_out, encoder_out_lens

			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(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 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(self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens):
			@@ -341,6 +392,14 @@
			decoder_out = decoder_outs[0]
			decoder_out = torch.log_softmax(decoder_out, dim=-1)
			return decoder_out, ys_pad_lens

			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

			def _extract_feats(
			self, speech: torch.Tensor, speech_lengths: torch.Tensor
			@@ -557,11 +616,11 @@
			self,
			vocab_size: int,
			token_list: Union[Tuple[str, ...], List[str]],
			frontend: Optional[AbsFrontend],
			specaug: Optional[AbsSpecAug],
			normalize: Optional[AbsNormalize],
			frontend: Optional[torch.nn.Module],
			specaug: Optional[torch.nn.Module],
			normalize: Optional[torch.nn.Module],
			preencoder: Optional[AbsPreEncoder],
			encoder: AbsEncoder,
			encoder: torch.nn.Module,
			postencoder: Optional[AbsPostEncoder],
			decoder: AbsDecoder,
			ctc: CTC,
			@@ -835,11 +894,11 @@
			self,
			vocab_size: int,
			token_list: Union[Tuple[str, ...], List[str]],
			frontend: Optional[AbsFrontend],
			specaug: Optional[AbsSpecAug],
			normalize: Optional[AbsNormalize],
			frontend: Optional[torch.nn.Module],
			specaug: Optional[torch.nn.Module],
			normalize: Optional[torch.nn.Module],
			preencoder: Optional[AbsPreEncoder],
			encoder: AbsEncoder,
			encoder: torch.nn.Module,
			postencoder: Optional[AbsPostEncoder],
			decoder: AbsDecoder,
			ctc: CTC,
			@@ -926,12 +985,10 @@
			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_cif_peak = self.predictor.get_upsample_timestamp(encoder_out,
			ds_alphas, ds_cif_peak, us_alphas, us_peaks = self.predictor.get_upsample_timestamp(encoder_out,
			encoder_out_mask,
			token_num)

			import pdb; pdb.set_trace()
			return ds_alphas, ds_cif_peak, us_alphas, us_cif_peak
			return ds_alphas, ds_cif_peak, us_alphas, us_peaks

			def forward(
			self,
			@@ -964,18 +1021,572 @@

			# 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
			)

			loss = loss_pre2
			# 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
			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[torch.nn.Module],
			specaug: Optional[torch.nn.Module],
			normalize: Optional[torch.nn.Module],
			preencoder: Optional[AbsPreEncoder],
			encoder: torch.nn.Module,
			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