import logging
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from contextlib import contextmanager
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from distutils.version import LooseVersion
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from typing import Dict
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from typing import List
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from typing import Optional
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from typing import Tuple
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from typing import Union
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import numpy as np
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import torch
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from funasr.layers.abs_normalize import AbsNormalize
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from funasr.models.ctc import CTC
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from funasr.models.decoder.abs_decoder import AbsDecoder
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from funasr.models.encoder.abs_encoder import AbsEncoder
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from funasr.models.frontend.abs_frontend import AbsFrontend
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from funasr.models.postencoder.abs_postencoder import AbsPostEncoder
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from funasr.models.preencoder.abs_preencoder import AbsPreEncoder
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from funasr.models.specaug.abs_specaug import AbsSpecAug
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from funasr.modules.add_sos_eos import add_sos_eos
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from funasr.modules.nets_utils import make_pad_mask, pad_list
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from funasr.modules.nets_utils import th_accuracy
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from funasr.torch_utils.device_funcs import force_gatherable
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from funasr.models.e2e_asr_paraformer import Paraformer
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if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
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from torch.cuda.amp import autocast
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else:
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# Nothing to do if torch<1.6.0
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@contextmanager
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def autocast(enabled=True):
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yield
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class NeatContextualParaformer(Paraformer):
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def __init__(
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self,
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vocab_size: int,
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token_list: Union[Tuple[str, ...], List[str]],
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frontend: Optional[AbsFrontend],
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specaug: Optional[AbsSpecAug],
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normalize: Optional[AbsNormalize],
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encoder: AbsEncoder,
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decoder: AbsDecoder,
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ctc: CTC,
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ctc_weight: float = 0.5,
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interctc_weight: float = 0.0,
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ignore_id: int = -1,
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blank_id: int = 0,
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sos: int = 1,
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eos: int = 2,
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lsm_weight: float = 0.0,
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length_normalized_loss: bool = False,
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report_cer: bool = True,
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report_wer: bool = True,
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sym_space: str = "<space>",
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sym_blank: str = "<blank>",
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extract_feats_in_collect_stats: bool = True,
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predictor = None,
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predictor_weight: float = 0.0,
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predictor_bias: int = 0,
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sampling_ratio: float = 0.2,
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target_buffer_length: int = -1,
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inner_dim: int = 256,
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bias_encoder_type: str = 'lstm',
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use_decoder_embedding: bool = False,
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crit_attn_weight: float = 0.0,
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crit_attn_smooth: float = 0.0,
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bias_encoder_dropout_rate: float = 0.0,
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preencoder: Optional[AbsPreEncoder] = None,
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postencoder: Optional[AbsPostEncoder] = None,
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):
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assert 0.0 <= ctc_weight <= 1.0, ctc_weight
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assert 0.0 <= interctc_weight < 1.0, interctc_weight
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super().__init__(
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vocab_size=vocab_size,
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token_list=token_list,
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frontend=frontend,
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specaug=specaug,
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normalize=normalize,
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preencoder=preencoder,
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encoder=encoder,
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postencoder=postencoder,
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decoder=decoder,
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ctc=ctc,
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ctc_weight=ctc_weight,
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interctc_weight=interctc_weight,
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ignore_id=ignore_id,
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blank_id=blank_id,
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sos=sos,
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eos=eos,
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lsm_weight=lsm_weight,
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length_normalized_loss=length_normalized_loss,
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report_cer=report_cer,
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report_wer=report_wer,
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sym_space=sym_space,
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sym_blank=sym_blank,
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extract_feats_in_collect_stats=extract_feats_in_collect_stats,
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predictor=predictor,
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predictor_weight=predictor_weight,
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predictor_bias=predictor_bias,
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sampling_ratio=sampling_ratio,
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)
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if bias_encoder_type == 'lstm':
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logging.warning("enable bias encoder sampling and contextual training")
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self.bias_encoder = torch.nn.LSTM(inner_dim, inner_dim, 1, batch_first=True, dropout=bias_encoder_dropout_rate)
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self.bias_embed = torch.nn.Embedding(vocab_size, inner_dim)
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elif bias_encoder_type == 'mean':
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logging.warning("enable bias encoder sampling and contextual training")
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self.bias_embed = torch.nn.Embedding(vocab_size, inner_dim)
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else:
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logging.error("Unsupport bias encoder type: {}".format(bias_encoder_type))
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self.target_buffer_length = target_buffer_length
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if self.target_buffer_length > 0:
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self.hotword_buffer = None
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self.length_record = []
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self.current_buffer_length = 0
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self.use_decoder_embedding = use_decoder_embedding
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self.crit_attn_weight = crit_attn_weight
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if self.crit_attn_weight > 0:
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self.attn_loss = torch.nn.L1Loss()
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self.crit_attn_smooth = crit_attn_smooth
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def forward(
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self,
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speech: torch.Tensor,
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speech_lengths: torch.Tensor,
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text: torch.Tensor,
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text_lengths: torch.Tensor,
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hotword_pad: torch.Tensor,
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hotword_lengths: torch.Tensor,
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ideal_attn: torch.Tensor,
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) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
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"""Frontend + Encoder + Decoder + Calc loss
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Args:
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speech: (Batch, Length, ...)
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speech_lengths: (Batch, )
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text: (Batch, Length)
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text_lengths: (Batch,)
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"""
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assert text_lengths.dim() == 1, text_lengths.shape
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# Check that batch_size is unified
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assert (
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speech.shape[0]
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== speech_lengths.shape[0]
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== text.shape[0]
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== text_lengths.shape[0]
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), (speech.shape, speech_lengths.shape, text.shape, text_lengths.shape)
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batch_size = speech.shape[0]
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self.step_cur += 1
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# for data-parallel
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text = text[:, : text_lengths.max()]
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speech = speech[:, :speech_lengths.max()]
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# 1. Encoder
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encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
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intermediate_outs = None
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if isinstance(encoder_out, tuple):
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intermediate_outs = encoder_out[1]
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encoder_out = encoder_out[0]
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loss_att, acc_att, cer_att, wer_att = None, None, None, None
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loss_ctc, cer_ctc = None, None
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loss_pre = None
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loss_ideal = None
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stats = dict()
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# 1. CTC branch
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if self.ctc_weight != 0.0:
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loss_ctc, cer_ctc = self._calc_ctc_loss(
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encoder_out, encoder_out_lens, text, text_lengths
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)
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# Collect CTC branch stats
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stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None
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stats["cer_ctc"] = cer_ctc
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# Intermediate CTC (optional)
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loss_interctc = 0.0
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if self.interctc_weight != 0.0 and intermediate_outs is not None:
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for layer_idx, intermediate_out in intermediate_outs:
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# we assume intermediate_out has the same length & padding
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# as those of encoder_out
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loss_ic, cer_ic = self._calc_ctc_loss(
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intermediate_out, encoder_out_lens, text, text_lengths
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)
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loss_interctc = loss_interctc + loss_ic
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# Collect Intermedaite CTC stats
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stats["loss_interctc_layer{}".format(layer_idx)] = (
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loss_ic.detach() if loss_ic is not None else None
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)
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stats["cer_interctc_layer{}".format(layer_idx)] = cer_ic
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loss_interctc = loss_interctc / len(intermediate_outs)
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# calculate whole encoder loss
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loss_ctc = (1 - self.interctc_weight) * loss_ctc + self.interctc_weight * loss_interctc
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# 2b. Attention decoder branch
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if self.ctc_weight != 1.0:
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loss_att, acc_att, cer_att, wer_att, loss_pre, loss_ideal = self._calc_att_clas_loss(
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encoder_out, encoder_out_lens, text, text_lengths, hotword_pad, hotword_lengths, ideal_attn
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)
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# 3. CTC-Att loss definition
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if self.ctc_weight == 0.0:
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loss = loss_att + loss_pre * self.predictor_weight
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elif self.ctc_weight == 1.0:
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loss = loss_ctc
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else:
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loss = self.ctc_weight * loss_ctc + (1 - self.ctc_weight) * loss_att + loss_pre * self.predictor_weight
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if loss_ideal is not None:
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loss = loss + loss_ideal * self.crit_attn_weight
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stats["loss_ideal"] = loss_ideal.detach().cpu()
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# Collect Attn branch stats
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stats["loss_att"] = loss_att.detach() if loss_att is not None else None
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stats["acc"] = acc_att
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stats["cer"] = cer_att
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stats["wer"] = wer_att
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stats["loss_pre"] = loss_pre.detach().cpu() if loss_pre is not None else None
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stats["loss"] = torch.clone(loss.detach())
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# force_gatherable: to-device and to-tensor if scalar for DataParallel
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loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
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return loss, stats, weight
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def _calc_att_clas_loss(
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self,
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encoder_out: torch.Tensor,
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encoder_out_lens: torch.Tensor,
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ys_pad: torch.Tensor,
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ys_pad_lens: torch.Tensor,
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hotword_pad: torch.Tensor,
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hotword_lengths: torch.Tensor,
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ideal_attn: torch.Tensor,
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):
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encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
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encoder_out.device)
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if self.predictor_bias == 1:
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_, ys_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
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ys_pad_lens = ys_pad_lens + self.predictor_bias
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pre_acoustic_embeds, pre_token_length, _, _ = self.predictor(encoder_out, ys_pad, encoder_out_mask,
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ignore_id=self.ignore_id)
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# -1. bias encoder
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if self.use_decoder_embedding:
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hw_embed = self.decoder.embed(hotword_pad)
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else:
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hw_embed = self.bias_embed(hotword_pad)
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hw_embed, (_, _) = self.bias_encoder(hw_embed)
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_ind = np.arange(0, hotword_pad.shape[0]).tolist()
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selected = hw_embed[_ind, [i-1 for i in hotword_lengths.detach().cpu().tolist()]]
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contextual_info = selected.squeeze(0).repeat(ys_pad.shape[0], 1, 1).to(ys_pad.device)
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# 0. sampler
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decoder_out_1st = None
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if self.sampling_ratio > 0.0:
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if self.step_cur < 2:
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logging.info("enable sampler in paraformer, sampling_ratio: {}".format(self.sampling_ratio))
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sematic_embeds, decoder_out_1st = self.sampler(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens,
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pre_acoustic_embeds, contextual_info)
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else:
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if self.step_cur < 2:
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logging.info("disable sampler in paraformer, sampling_ratio: {}".format(self.sampling_ratio))
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sematic_embeds = pre_acoustic_embeds
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# 1. Forward decoder
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decoder_outs = self.decoder(
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encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, contextual_info=contextual_info
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)
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decoder_out, _ = decoder_outs[0], decoder_outs[1]
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'''
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if self.crit_attn_weight > 0 and attn.shape[-1] > 1:
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ideal_attn = ideal_attn + self.crit_attn_smooth / (self.crit_attn_smooth + 1.0)
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attn_non_blank = attn[:,:,:,:-1]
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ideal_attn_non_blank = ideal_attn[:,:,:-1]
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loss_ideal = self.attn_loss(attn_non_blank.max(1)[0], ideal_attn_non_blank.to(attn.device))
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else:
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loss_ideal = None
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'''
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loss_ideal = None
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if decoder_out_1st is None:
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decoder_out_1st = decoder_out
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# 2. Compute attention loss
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loss_att = self.criterion_att(decoder_out, ys_pad)
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acc_att = th_accuracy(
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decoder_out_1st.view(-1, self.vocab_size),
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ys_pad,
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ignore_label=self.ignore_id,
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)
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loss_pre = self.criterion_pre(ys_pad_lens.type_as(pre_token_length), pre_token_length)
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# Compute cer/wer using attention-decoder
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if self.training or self.error_calculator is None:
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cer_att, wer_att = None, None
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else:
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ys_hat = decoder_out_1st.argmax(dim=-1)
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cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu())
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return loss_att, acc_att, cer_att, wer_att, loss_pre, loss_ideal
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def sampler(self, encoder_out, encoder_out_lens, ys_pad, ys_pad_lens, pre_acoustic_embeds, contextual_info):
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tgt_mask = (~make_pad_mask(ys_pad_lens, maxlen=ys_pad_lens.max())[:, :, None]).to(ys_pad.device)
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ys_pad = ys_pad * tgt_mask[:, :, 0]
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if self.share_embedding:
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ys_pad_embed = self.decoder.output_layer.weight[ys_pad]
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else:
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ys_pad_embed = self.decoder.embed(ys_pad)
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with torch.no_grad():
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decoder_outs = self.decoder(
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encoder_out, encoder_out_lens, pre_acoustic_embeds, ys_pad_lens, contextual_info=contextual_info
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)
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decoder_out, _ = decoder_outs[0], decoder_outs[1]
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pred_tokens = decoder_out.argmax(-1)
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nonpad_positions = ys_pad.ne(self.ignore_id)
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seq_lens = (nonpad_positions).sum(1)
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same_num = ((pred_tokens == ys_pad) & nonpad_positions).sum(1)
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input_mask = torch.ones_like(nonpad_positions)
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bsz, seq_len = ys_pad.size()
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for li in range(bsz):
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target_num = (((seq_lens[li] - same_num[li].sum()).float()) * self.sampling_ratio).long()
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if target_num > 0:
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input_mask[li].scatter_(dim=0, index=torch.randperm(seq_lens[li])[:target_num].to(pre_acoustic_embeds.device), value=0)
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input_mask = input_mask.eq(1)
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input_mask = input_mask.masked_fill(~nonpad_positions, False)
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input_mask_expand_dim = input_mask.unsqueeze(2).to(pre_acoustic_embeds.device)
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sematic_embeds = pre_acoustic_embeds.masked_fill(~input_mask_expand_dim, 0) + ys_pad_embed.masked_fill(
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input_mask_expand_dim, 0)
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return sematic_embeds * tgt_mask, decoder_out * tgt_mask
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def cal_decoder_with_predictor(self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, hw_list=None, clas_scale=1.0):
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if hw_list is None:
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hw_list = [torch.Tensor([1]).long().to(encoder_out.device)] # empty hotword list
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hw_list_pad = pad_list(hw_list, 0)
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if self.use_decoder_embedding:
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hw_embed = self.decoder.embed(hw_list_pad)
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else:
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hw_embed = self.bias_embed(hw_list_pad)
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hw_embed, (h_n, _) = self.bias_encoder(hw_embed)
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hw_embed = h_n.repeat(encoder_out.shape[0], 1, 1)
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else:
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hw_lengths = [len(i) for i in hw_list]
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hw_list_pad = pad_list([torch.Tensor(i).long() for i in hw_list], 0).to(encoder_out.device)
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if self.use_decoder_embedding:
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hw_embed = self.decoder.embed(hw_list_pad)
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else:
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hw_embed = self.bias_embed(hw_list_pad)
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hw_embed = torch.nn.utils.rnn.pack_padded_sequence(hw_embed, hw_lengths, batch_first=True,
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enforce_sorted=False)
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_, (h_n, _) = self.bias_encoder(hw_embed)
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hw_embed = h_n.repeat(encoder_out.shape[0], 1, 1)
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decoder_outs = self.decoder(
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encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, contextual_info=hw_embed, clas_scale=clas_scale
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)
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decoder_out = decoder_outs[0]
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decoder_out = torch.log_softmax(decoder_out, dim=-1)
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return decoder_out, ys_pad_lens
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