#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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import time
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import torch
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import logging
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from torch.cuda.amp import autocast
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from typing import Union, Dict, List, Tuple, Optional
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from funasr.register import tables
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from funasr.models.ctc.ctc import CTC
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from funasr.utils import postprocess_utils
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from funasr.metrics.compute_acc import th_accuracy
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from funasr.utils.datadir_writer import DatadirWriter
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from funasr.models.paraformer.search import Hypothesis
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from funasr.models.paraformer.cif_predictor import mae_loss
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from funasr.train_utils.device_funcs import force_gatherable
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from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
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from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
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from funasr.models.transformer.utils.nets_utils import make_pad_mask, pad_list
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from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
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@tables.register("model_classes", "UniASR")
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class UniASR(torch.nn.Module):
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"""
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Author: Speech Lab of DAMO Academy, Alibaba Group
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"""
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def __init__(
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self,
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specaug: Optional[str] = None,
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specaug_conf: Optional[Dict] = None,
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normalize: str = None,
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normalize_conf: Optional[Dict] = None,
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encoder: str = None,
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encoder_conf: Optional[Dict] = None,
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decoder: str = None,
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decoder_conf: Optional[Dict] = None,
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ctc: str = None,
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ctc_conf: Optional[Dict] = None,
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predictor: str = None,
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predictor_conf: Optional[Dict] = None,
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ctc_weight: float = 0.5,
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input_size: int = 80,
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vocab_size: int = -1,
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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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share_embedding: bool = False,
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# preencoder: Optional[AbsPreEncoder] = None,
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# postencoder: Optional[AbsPostEncoder] = None,
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use_1st_decoder_loss: bool = False,
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encoder1_encoder2_joint_training: bool = True,
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**kwargs,
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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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self.blank_id = 0
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self.sos = 1
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self.eos = 2
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self.vocab_size = vocab_size
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self.ignore_id = ignore_id
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self.ctc_weight = ctc_weight
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self.interctc_weight = interctc_weight
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self.token_list = token_list.copy()
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self.frontend = frontend
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self.specaug = specaug
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self.normalize = normalize
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self.preencoder = preencoder
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self.postencoder = postencoder
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self.encoder = encoder
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if not hasattr(self.encoder, "interctc_use_conditioning"):
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self.encoder.interctc_use_conditioning = False
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if self.encoder.interctc_use_conditioning:
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self.encoder.conditioning_layer = torch.nn.Linear(
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vocab_size, self.encoder.output_size()
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)
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self.error_calculator = None
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# we set self.decoder = None in the CTC mode since
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# self.decoder parameters were never used and PyTorch complained
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# and threw an Exception in the multi-GPU experiment.
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# thanks Jeff Farris for pointing out the issue.
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if ctc_weight == 1.0:
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self.decoder = None
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else:
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self.decoder = decoder
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self.criterion_att = LabelSmoothingLoss(
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size=vocab_size,
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padding_idx=ignore_id,
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smoothing=lsm_weight,
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normalize_length=length_normalized_loss,
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)
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if report_cer or report_wer:
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self.error_calculator = ErrorCalculator(
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token_list, sym_space, sym_blank, report_cer, report_wer
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)
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if ctc_weight == 0.0:
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self.ctc = None
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else:
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self.ctc = ctc
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self.extract_feats_in_collect_stats = extract_feats_in_collect_stats
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self.predictor = predictor
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self.predictor_weight = predictor_weight
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self.criterion_pre = mae_loss(normalize_length=length_normalized_loss)
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self.step_cur = 0
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if self.encoder.overlap_chunk_cls is not None:
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from funasr.models.scama.chunk_utilis import build_scama_mask_for_cross_attention_decoder
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self.build_scama_mask_for_cross_attention_decoder_fn = build_scama_mask_for_cross_attention_decoder
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self.decoder_attention_chunk_type = decoder_attention_chunk_type
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self.encoder2 = encoder2
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self.decoder2 = decoder2
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self.ctc_weight2 = ctc_weight2
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if ctc_weight2 == 0.0:
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self.ctc2 = None
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else:
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self.ctc2 = ctc2
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self.interctc_weight2 = interctc_weight2
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self.predictor2 = predictor2
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self.predictor_weight2 = predictor_weight2
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self.decoder_attention_chunk_type2 = decoder_attention_chunk_type2
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self.stride_conv = stride_conv
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self.loss_weight_model1 = loss_weight_model1
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if self.encoder2.overlap_chunk_cls is not None:
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from funasr.models.scama.chunk_utilis import build_scama_mask_for_cross_attention_decoder
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self.build_scama_mask_for_cross_attention_decoder_fn2 = build_scama_mask_for_cross_attention_decoder
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self.decoder_attention_chunk_type2 = decoder_attention_chunk_type2
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self.enable_maas_finetune = enable_maas_finetune
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self.freeze_encoder2 = freeze_encoder2
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self.encoder1_encoder2_joint_training = encoder1_encoder2_joint_training
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self.length_normalized_loss = length_normalized_loss
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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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decoding_ind: int = None,
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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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# 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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ind = self.encoder.overlap_chunk_cls.random_choice(self.training, decoding_ind)
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# 1. Encoder
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if self.enable_maas_finetune:
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with torch.no_grad():
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speech_raw, encoder_out, encoder_out_lens = self.encode(speech, speech_lengths, ind=ind)
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else:
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speech_raw, encoder_out, encoder_out_lens = self.encode(speech, speech_lengths, ind=ind)
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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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stats = dict()
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loss_pre = None
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loss, loss1, loss2 = 0.0, 0.0, 0.0
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if self.loss_weight_model1 > 0.0:
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## model1
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# 1. CTC branch
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if self.enable_maas_finetune:
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with torch.no_grad():
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if self.ctc_weight != 0.0:
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if self.encoder.overlap_chunk_cls is not None:
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encoder_out_ctc, encoder_out_lens_ctc = self.encoder.overlap_chunk_cls.remove_chunk(encoder_out,
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encoder_out_lens,
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chunk_outs=None)
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loss_ctc, cer_ctc = self._calc_ctc_loss(
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encoder_out_ctc, encoder_out_lens_ctc, 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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if self.encoder.overlap_chunk_cls is not None:
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encoder_out_ctc, encoder_out_lens_ctc = \
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self.encoder.overlap_chunk_cls.remove_chunk(
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intermediate_out,
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encoder_out_lens,
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chunk_outs=None)
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loss_ic, cer_ic = self._calc_ctc_loss(
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encoder_out_ctc, encoder_out_lens_ctc, 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 = (
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1 - self.interctc_weight
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) * 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 = self._calc_att_predictor_loss(
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encoder_out, encoder_out_lens, text, text_lengths
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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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# 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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else:
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if self.ctc_weight != 0.0:
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if self.encoder.overlap_chunk_cls is not None:
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encoder_out_ctc, encoder_out_lens_ctc = self.encoder.overlap_chunk_cls.remove_chunk(encoder_out,
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encoder_out_lens,
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chunk_outs=None)
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loss_ctc, cer_ctc = self._calc_ctc_loss(
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encoder_out_ctc, encoder_out_lens_ctc, 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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if self.encoder.overlap_chunk_cls is not None:
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encoder_out_ctc, encoder_out_lens_ctc = \
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self.encoder.overlap_chunk_cls.remove_chunk(
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intermediate_out,
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encoder_out_lens,
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chunk_outs=None)
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loss_ic, cer_ic = self._calc_ctc_loss(
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encoder_out_ctc, encoder_out_lens_ctc, 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 = (
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1 - self.interctc_weight
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) * 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 = self._calc_att_predictor_loss(
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encoder_out, encoder_out_lens, text, text_lengths
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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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# 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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loss1 = loss
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if self.loss_weight_model1 < 1.0:
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## model2
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# encoder2
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if self.freeze_encoder2:
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with torch.no_grad():
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encoder_out, encoder_out_lens = self.encode2(encoder_out, encoder_out_lens, speech_raw, speech_lengths, ind=ind)
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else:
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encoder_out, encoder_out_lens = self.encode2(encoder_out, encoder_out_lens, speech_raw, speech_lengths, ind=ind)
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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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# CTC2
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if self.ctc_weight2 != 0.0:
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if self.encoder2.overlap_chunk_cls is not None:
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encoder_out_ctc, encoder_out_lens_ctc = \
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self.encoder2.overlap_chunk_cls.remove_chunk(
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encoder_out,
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encoder_out_lens,
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chunk_outs=None,
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)
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loss_ctc, cer_ctc = self._calc_ctc_loss2(
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encoder_out_ctc, encoder_out_lens_ctc, text, text_lengths
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)
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# Collect CTC branch stats
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stats["loss_ctc2"] = loss_ctc.detach() if loss_ctc is not None else None
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stats["cer_ctc2"] = cer_ctc
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# Intermediate CTC (optional)
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loss_interctc = 0.0
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if self.interctc_weight2 != 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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if self.encoder2.overlap_chunk_cls is not None:
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encoder_out_ctc, encoder_out_lens_ctc = \
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self.encoder2.overlap_chunk_cls.remove_chunk(
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intermediate_out,
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encoder_out_lens,
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chunk_outs=None)
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loss_ic, cer_ic = self._calc_ctc_loss2(
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encoder_out_ctc, encoder_out_lens_ctc, 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{}2".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{}2".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 = (
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1 - self.interctc_weight2
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) * loss_ctc + self.interctc_weight2 * loss_interctc
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# 2b. Attention decoder branch
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if self.ctc_weight2 != 1.0:
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loss_att, acc_att, cer_att, wer_att, loss_pre = self._calc_att_predictor_loss2(
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encoder_out, encoder_out_lens, text, text_lengths
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)
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# 3. CTC-Att loss definition
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if self.ctc_weight2 == 0.0:
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loss = loss_att + loss_pre * self.predictor_weight2
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elif self.ctc_weight2 == 1.0:
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loss = loss_ctc
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else:
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loss = self.ctc_weight2 * loss_ctc + (
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1 - self.ctc_weight2) * loss_att + loss_pre * self.predictor_weight2
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# Collect Attn branch stats
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stats["loss_att2"] = loss_att.detach() if loss_att is not None else None
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stats["acc2"] = acc_att
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stats["cer2"] = cer_att
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stats["wer2"] = wer_att
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stats["loss_pre2"] = loss_pre.detach().cpu() if loss_pre is not None else None
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loss2 = loss
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loss = loss1 * self.loss_weight_model1 + loss2 * (1 - self.loss_weight_model1)
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stats["loss1"] = torch.clone(loss1.detach())
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stats["loss2"] = torch.clone(loss2.detach())
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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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if self.length_normalized_loss:
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batch_size = int((text_lengths + 1).sum())
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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 collect_feats(
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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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) -> Dict[str, torch.Tensor]:
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if self.extract_feats_in_collect_stats:
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feats, feats_lengths = self._extract_feats(speech, speech_lengths)
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else:
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# Generate dummy stats if extract_feats_in_collect_stats is False
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logging.warning(
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"Generating dummy stats for feats and feats_lengths, "
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"because encoder_conf.extract_feats_in_collect_stats is "
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f"{self.extract_feats_in_collect_stats}"
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)
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feats, feats_lengths = speech, speech_lengths
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return {"feats": feats, "feats_lengths": feats_lengths}
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def encode(
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self, speech: torch.Tensor, speech_lengths: torch.Tensor, ind: int = 0,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Frontend + Encoder. Note that this method is used by asr_inference.py
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Args:
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speech: (Batch, Length, ...)
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speech_lengths: (Batch, )
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"""
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with autocast(False):
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# 1. Extract feats
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feats, feats_lengths = self._extract_feats(speech, speech_lengths)
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# 2. Data augmentation
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if self.specaug is not None and self.training:
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feats, feats_lengths = self.specaug(feats, feats_lengths)
|
|
# 3. Normalization for feature: e.g. Global-CMVN, Utterance-CMVN
|
if self.normalize is not None:
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feats, feats_lengths = self.normalize(feats, feats_lengths)
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speech_raw = feats.clone().to(feats.device)
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# Pre-encoder, e.g. used for raw input data
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if self.preencoder is not None:
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feats, feats_lengths = self.preencoder(feats, feats_lengths)
|
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# 4. Forward encoder
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# feats: (Batch, Length, Dim)
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# -> encoder_out: (Batch, Length2, Dim2)
|
if self.encoder.interctc_use_conditioning:
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encoder_out, encoder_out_lens, _ = self.encoder(
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feats, feats_lengths, ctc=self.ctc, ind=ind
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)
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else:
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encoder_out, encoder_out_lens, _ = self.encoder(feats, feats_lengths, ind=ind)
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intermediate_outs = None
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if isinstance(encoder_out, tuple):
|
intermediate_outs = encoder_out[1]
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encoder_out = encoder_out[0]
|
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# Post-encoder, e.g. NLU
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if self.postencoder is not None:
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encoder_out, encoder_out_lens = self.postencoder(
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encoder_out, encoder_out_lens
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)
|
|
assert encoder_out.size(0) == speech.size(0), (
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encoder_out.size(),
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speech.size(0),
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)
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assert encoder_out.size(1) <= encoder_out_lens.max(), (
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encoder_out.size(),
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encoder_out_lens.max(),
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)
|
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if intermediate_outs is not None:
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return (encoder_out, intermediate_outs), encoder_out_lens
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return speech_raw, encoder_out, encoder_out_lens
|
|
def encode2(
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self,
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encoder_out: torch.Tensor,
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encoder_out_lens: torch.Tensor,
|
speech: torch.Tensor,
|
speech_lengths: torch.Tensor,
|
ind: int = 0,
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
"""Frontend + Encoder. Note that this method is used by asr_inference.py
|
Args:
|
speech: (Batch, Length, ...)
|
speech_lengths: (Batch, )
|
"""
|
# with autocast(False):
|
# # 1. Extract feats
|
# feats, feats_lengths = self._extract_feats(speech, speech_lengths)
|
#
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# # 2. Data augmentation
|
# if self.specaug is not None and self.training:
|
# feats, feats_lengths = self.specaug(feats, feats_lengths)
|
#
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# # 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)
|
encoder_out_rm, encoder_out_lens_rm = self.encoder.overlap_chunk_cls.remove_chunk(
|
encoder_out,
|
encoder_out_lens,
|
chunk_outs=None,
|
)
|
# residual_input
|
encoder_out = torch.cat((speech, encoder_out_rm), dim=-1)
|
encoder_out_lens = encoder_out_lens_rm
|
if self.stride_conv is not None:
|
speech, speech_lengths = self.stride_conv(encoder_out, encoder_out_lens)
|
if not self.encoder1_encoder2_joint_training:
|
speech = speech.detach()
|
speech_lengths = speech_lengths.detach()
|
# 4. Forward encoder
|
# feats: (Batch, Length, Dim)
|
# -> encoder_out: (Batch, Length2, Dim2)
|
if self.encoder2.interctc_use_conditioning:
|
encoder_out, encoder_out_lens, _ = self.encoder2(
|
speech, speech_lengths, ctc=self.ctc2, ind=ind
|
)
|
else:
|
encoder_out, encoder_out_lens, _ = self.encoder2(speech, speech_lengths, ind=ind)
|
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 _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,
|
):
|
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
|
)
|
|
# 2. Compute attention loss
|
loss_att = self.criterion_att(decoder_out, ys_out_pad)
|
acc_att = th_accuracy(
|
decoder_out.view(-1, self.vocab_size),
|
ys_out_pad,
|
ignore_label=self.ignore_id,
|
)
|
|
# 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.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
|
|
def _calc_att_predictor_loss(
|
self,
|
encoder_out: torch.Tensor,
|
encoder_out_lens: torch.Tensor,
|
ys_pad: torch.Tensor,
|
ys_pad_lens: torch.Tensor,
|
):
|
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
|
|
encoder_out_mask = sequence_mask(encoder_out_lens, maxlen=encoder_out.size(1), dtype=encoder_out.dtype,
|
device=encoder_out.device)[:, None, :]
|
mask_chunk_predictor = None
|
if self.encoder.overlap_chunk_cls is not None:
|
mask_chunk_predictor = self.encoder.overlap_chunk_cls.get_mask_chunk_predictor(None,
|
device=encoder_out.device,
|
batch_size=encoder_out.size(
|
0))
|
mask_shfit_chunk = self.encoder.overlap_chunk_cls.get_mask_shfit_chunk(None, device=encoder_out.device,
|
batch_size=encoder_out.size(0))
|
encoder_out = encoder_out * mask_shfit_chunk
|
pre_acoustic_embeds, pre_token_length, pre_alphas, _ = self.predictor(encoder_out,
|
ys_out_pad,
|
encoder_out_mask,
|
ignore_id=self.ignore_id,
|
mask_chunk_predictor=mask_chunk_predictor,
|
target_label_length=ys_in_lens,
|
)
|
predictor_alignments, predictor_alignments_len = self.predictor.gen_frame_alignments(pre_alphas,
|
encoder_out_lens)
|
|
scama_mask = None
|
if self.encoder.overlap_chunk_cls is not None and self.decoder_attention_chunk_type == 'chunk':
|
encoder_chunk_size = self.encoder.overlap_chunk_cls.chunk_size_pad_shift_cur
|
attention_chunk_center_bias = 0
|
attention_chunk_size = encoder_chunk_size
|
decoder_att_look_back_factor = self.encoder.overlap_chunk_cls.decoder_att_look_back_factor_cur
|
mask_shift_att_chunk_decoder = self.encoder.overlap_chunk_cls.get_mask_shift_att_chunk_decoder(None,
|
device=encoder_out.device,
|
batch_size=encoder_out.size(
|
0))
|
scama_mask = self.build_scama_mask_for_cross_attention_decoder_fn(
|
predictor_alignments=predictor_alignments,
|
encoder_sequence_length=encoder_out_lens,
|
chunk_size=1,
|
encoder_chunk_size=encoder_chunk_size,
|
attention_chunk_center_bias=attention_chunk_center_bias,
|
attention_chunk_size=attention_chunk_size,
|
attention_chunk_type=self.decoder_attention_chunk_type,
|
step=None,
|
predictor_mask_chunk_hopping=mask_chunk_predictor,
|
decoder_att_look_back_factor=decoder_att_look_back_factor,
|
mask_shift_att_chunk_decoder=mask_shift_att_chunk_decoder,
|
target_length=ys_in_lens,
|
is_training=self.training,
|
)
|
elif self.encoder.overlap_chunk_cls is not None:
|
encoder_out, encoder_out_lens = self.encoder.overlap_chunk_cls.remove_chunk(encoder_out, encoder_out_lens,
|
chunk_outs=None)
|
# try:
|
# 1. Forward decoder
|
decoder_out, _ = self.decoder(
|
encoder_out,
|
encoder_out_lens,
|
ys_in_pad,
|
ys_in_lens,
|
chunk_mask=scama_mask,
|
pre_acoustic_embeds=pre_acoustic_embeds,
|
|
)
|
|
# 2. Compute attention loss
|
loss_att = self.criterion_att(decoder_out, ys_out_pad)
|
acc_att = th_accuracy(
|
decoder_out.view(-1, self.vocab_size),
|
ys_out_pad,
|
ignore_label=self.ignore_id,
|
)
|
# predictor loss
|
loss_pre = self.criterion_pre(ys_in_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.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_att_predictor_loss2(
|
self,
|
encoder_out: torch.Tensor,
|
encoder_out_lens: torch.Tensor,
|
ys_pad: torch.Tensor,
|
ys_pad_lens: torch.Tensor,
|
):
|
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
|
|
encoder_out_mask = sequence_mask(encoder_out_lens, maxlen=encoder_out.size(1), dtype=encoder_out.dtype,
|
device=encoder_out.device)[:, None, :]
|
mask_chunk_predictor = None
|
if self.encoder2.overlap_chunk_cls is not None:
|
mask_chunk_predictor = self.encoder2.overlap_chunk_cls.get_mask_chunk_predictor(None,
|
device=encoder_out.device,
|
batch_size=encoder_out.size(
|
0))
|
mask_shfit_chunk = self.encoder2.overlap_chunk_cls.get_mask_shfit_chunk(None, device=encoder_out.device,
|
batch_size=encoder_out.size(0))
|
encoder_out = encoder_out * mask_shfit_chunk
|
pre_acoustic_embeds, pre_token_length, pre_alphas, _ = self.predictor2(encoder_out,
|
ys_out_pad,
|
encoder_out_mask,
|
ignore_id=self.ignore_id,
|
mask_chunk_predictor=mask_chunk_predictor,
|
target_label_length=ys_in_lens,
|
)
|
predictor_alignments, predictor_alignments_len = self.predictor2.gen_frame_alignments(pre_alphas,
|
encoder_out_lens)
|
|
scama_mask = None
|
if self.encoder2.overlap_chunk_cls is not None and self.decoder_attention_chunk_type2 == 'chunk':
|
encoder_chunk_size = self.encoder2.overlap_chunk_cls.chunk_size_pad_shift_cur
|
attention_chunk_center_bias = 0
|
attention_chunk_size = encoder_chunk_size
|
decoder_att_look_back_factor = self.encoder2.overlap_chunk_cls.decoder_att_look_back_factor_cur
|
mask_shift_att_chunk_decoder = self.encoder2.overlap_chunk_cls.get_mask_shift_att_chunk_decoder(None,
|
device=encoder_out.device,
|
batch_size=encoder_out.size(
|
0))
|
scama_mask = self.build_scama_mask_for_cross_attention_decoder_fn2(
|
predictor_alignments=predictor_alignments,
|
encoder_sequence_length=encoder_out_lens,
|
chunk_size=1,
|
encoder_chunk_size=encoder_chunk_size,
|
attention_chunk_center_bias=attention_chunk_center_bias,
|
attention_chunk_size=attention_chunk_size,
|
attention_chunk_type=self.decoder_attention_chunk_type2,
|
step=None,
|
predictor_mask_chunk_hopping=mask_chunk_predictor,
|
decoder_att_look_back_factor=decoder_att_look_back_factor,
|
mask_shift_att_chunk_decoder=mask_shift_att_chunk_decoder,
|
target_length=ys_in_lens,
|
is_training=self.training,
|
)
|
elif self.encoder2.overlap_chunk_cls is not None:
|
encoder_out, encoder_out_lens = self.encoder2.overlap_chunk_cls.remove_chunk(encoder_out, encoder_out_lens,
|
chunk_outs=None)
|
# try:
|
# 1. Forward decoder
|
decoder_out, _ = self.decoder2(
|
encoder_out,
|
encoder_out_lens,
|
ys_in_pad,
|
ys_in_lens,
|
chunk_mask=scama_mask,
|
pre_acoustic_embeds=pre_acoustic_embeds,
|
)
|
|
# 2. Compute attention loss
|
loss_att = self.criterion_att(decoder_out, ys_out_pad)
|
acc_att = th_accuracy(
|
decoder_out.view(-1, self.vocab_size),
|
ys_out_pad,
|
ignore_label=self.ignore_id,
|
)
|
# predictor loss
|
loss_pre = self.criterion_pre(ys_in_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.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_mask(
|
self,
|
encoder_out: torch.Tensor,
|
encoder_out_lens: torch.Tensor,
|
ys_pad: torch.Tensor = None,
|
ys_pad_lens: torch.Tensor = None,
|
):
|
# 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
|
ys_out_pad, ys_in_lens = None, None
|
|
encoder_out_mask = sequence_mask(encoder_out_lens, maxlen=encoder_out.size(1), dtype=encoder_out.dtype,
|
device=encoder_out.device)[:, None, :]
|
mask_chunk_predictor = None
|
if self.encoder.overlap_chunk_cls is not None:
|
mask_chunk_predictor = self.encoder.overlap_chunk_cls.get_mask_chunk_predictor(None,
|
device=encoder_out.device,
|
batch_size=encoder_out.size(
|
0))
|
mask_shfit_chunk = self.encoder.overlap_chunk_cls.get_mask_shfit_chunk(None, device=encoder_out.device,
|
batch_size=encoder_out.size(0))
|
encoder_out = encoder_out * mask_shfit_chunk
|
pre_acoustic_embeds, pre_token_length, pre_alphas, _ = self.predictor(encoder_out,
|
ys_out_pad,
|
encoder_out_mask,
|
ignore_id=self.ignore_id,
|
mask_chunk_predictor=mask_chunk_predictor,
|
target_label_length=ys_in_lens,
|
)
|
predictor_alignments, predictor_alignments_len = self.predictor.gen_frame_alignments(pre_alphas,
|
encoder_out_lens)
|
|
scama_mask = None
|
if self.encoder.overlap_chunk_cls is not None and self.decoder_attention_chunk_type == 'chunk':
|
encoder_chunk_size = self.encoder.overlap_chunk_cls.chunk_size_pad_shift_cur
|
attention_chunk_center_bias = 0
|
attention_chunk_size = encoder_chunk_size
|
decoder_att_look_back_factor = self.encoder.overlap_chunk_cls.decoder_att_look_back_factor_cur
|
mask_shift_att_chunk_decoder = self.encoder.overlap_chunk_cls.get_mask_shift_att_chunk_decoder(None,
|
device=encoder_out.device,
|
batch_size=encoder_out.size(
|
0))
|
scama_mask = self.build_scama_mask_for_cross_attention_decoder_fn(
|
predictor_alignments=predictor_alignments,
|
encoder_sequence_length=encoder_out_lens,
|
chunk_size=1,
|
encoder_chunk_size=encoder_chunk_size,
|
attention_chunk_center_bias=attention_chunk_center_bias,
|
attention_chunk_size=attention_chunk_size,
|
attention_chunk_type=self.decoder_attention_chunk_type,
|
step=None,
|
predictor_mask_chunk_hopping=mask_chunk_predictor,
|
decoder_att_look_back_factor=decoder_att_look_back_factor,
|
mask_shift_att_chunk_decoder=mask_shift_att_chunk_decoder,
|
target_length=ys_in_lens,
|
is_training=self.training,
|
)
|
elif self.encoder.overlap_chunk_cls is not None:
|
encoder_out, encoder_out_lens = self.encoder.overlap_chunk_cls.remove_chunk(encoder_out, encoder_out_lens,
|
chunk_outs=None)
|
|
return pre_acoustic_embeds, pre_token_length, predictor_alignments, predictor_alignments_len, scama_mask
|
|
def calc_predictor_mask2(
|
self,
|
encoder_out: torch.Tensor,
|
encoder_out_lens: torch.Tensor,
|
ys_pad: torch.Tensor = None,
|
ys_pad_lens: torch.Tensor = None,
|
):
|
# 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
|
ys_out_pad, ys_in_lens = None, None
|
|
encoder_out_mask = sequence_mask(encoder_out_lens, maxlen=encoder_out.size(1), dtype=encoder_out.dtype,
|
device=encoder_out.device)[:, None, :]
|
mask_chunk_predictor = None
|
if self.encoder2.overlap_chunk_cls is not None:
|
mask_chunk_predictor = self.encoder2.overlap_chunk_cls.get_mask_chunk_predictor(None,
|
device=encoder_out.device,
|
batch_size=encoder_out.size(
|
0))
|
mask_shfit_chunk = self.encoder2.overlap_chunk_cls.get_mask_shfit_chunk(None, device=encoder_out.device,
|
batch_size=encoder_out.size(0))
|
encoder_out = encoder_out * mask_shfit_chunk
|
pre_acoustic_embeds, pre_token_length, pre_alphas, _ = self.predictor2(encoder_out,
|
ys_out_pad,
|
encoder_out_mask,
|
ignore_id=self.ignore_id,
|
mask_chunk_predictor=mask_chunk_predictor,
|
target_label_length=ys_in_lens,
|
)
|
predictor_alignments, predictor_alignments_len = self.predictor2.gen_frame_alignments(pre_alphas,
|
encoder_out_lens)
|
|
scama_mask = None
|
if self.encoder2.overlap_chunk_cls is not None and self.decoder_attention_chunk_type2 == 'chunk':
|
encoder_chunk_size = self.encoder2.overlap_chunk_cls.chunk_size_pad_shift_cur
|
attention_chunk_center_bias = 0
|
attention_chunk_size = encoder_chunk_size
|
decoder_att_look_back_factor = self.encoder2.overlap_chunk_cls.decoder_att_look_back_factor_cur
|
mask_shift_att_chunk_decoder = self.encoder2.overlap_chunk_cls.get_mask_shift_att_chunk_decoder(None,
|
device=encoder_out.device,
|
batch_size=encoder_out.size(
|
0))
|
scama_mask = self.build_scama_mask_for_cross_attention_decoder_fn2(
|
predictor_alignments=predictor_alignments,
|
encoder_sequence_length=encoder_out_lens,
|
chunk_size=1,
|
encoder_chunk_size=encoder_chunk_size,
|
attention_chunk_center_bias=attention_chunk_center_bias,
|
attention_chunk_size=attention_chunk_size,
|
attention_chunk_type=self.decoder_attention_chunk_type2,
|
step=None,
|
predictor_mask_chunk_hopping=mask_chunk_predictor,
|
decoder_att_look_back_factor=decoder_att_look_back_factor,
|
mask_shift_att_chunk_decoder=mask_shift_att_chunk_decoder,
|
target_length=ys_in_lens,
|
is_training=self.training,
|
)
|
elif self.encoder2.overlap_chunk_cls is not None:
|
encoder_out, encoder_out_lens = self.encoder2.overlap_chunk_cls.remove_chunk(encoder_out, encoder_out_lens,
|
chunk_outs=None)
|
|
return pre_acoustic_embeds, pre_token_length, predictor_alignments, predictor_alignments_len, scama_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
|
|
def _calc_ctc_loss2(
|
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.ctc2(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.ctc2.argmax(encoder_out).data
|
cer_ctc = self.error_calculator(ys_hat.cpu(), ys_pad.cpu(), is_ctc=True)
|
return loss_ctc, cer_ctc
|
