mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
656 lines
22 KiB
Python
656 lines
22 KiB
Python
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 torch
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import torch.nn as nn
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import random
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import numpy as np
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# from funasr.layers.abs_normalize import AbsNormalize
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from funasr.losses.label_smoothing_loss import (
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LabelSmoothingLoss, # noqa: H301
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)
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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.e2e_asr_common import ErrorCalculator
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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.predictor.cif import mae_loss
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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.base_model import FunASRModel
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# from funasr.models.predictor.cif import CifPredictorV3
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from funasr.cli.model_class_factory import *
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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 Paraformer(nn.Module):
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"""
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Author: Speech Lab of DAMO Academy, Alibaba Group
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Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
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https://arxiv.org/abs/2206.08317
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"""
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def __init__(
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self,
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# token_list: Union[Tuple[str, ...], List[str]],
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frontend: Optional[str] = None,
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frontend_conf: Optional[Dict] = None,
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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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interctc_weight: float = 0.0,
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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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**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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# import pdb;
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# pdb.set_trace()
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if frontend is not None:
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frontend_class = frontend_choices.get_class(frontend)
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frontend = frontend_class(**frontend_conf)
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if specaug is not None:
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specaug_class = specaug_choices.get_class(specaug)
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specaug = specaug_class(**specaug_conf)
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if normalize is not None:
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normalize_class = normalize_choices.get_class(normalize)
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normalize = normalize_class(**normalize_conf)
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encoder_class = encoder_choices.get_class(encoder)
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encoder = encoder_class(input_size=input_size, **encoder_conf)
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encoder_output_size = encoder.output_size()
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if decoder is not None:
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decoder_class = decoder_choices.get_class(decoder)
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decoder = decoder_class(
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vocab_size=vocab_size,
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encoder_output_size=encoder_output_size,
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**decoder_conf,
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)
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if ctc_weight > 0.0:
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if ctc_conf is None:
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ctc_conf = {}
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ctc = CTC(
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odim=vocab_size, encoder_output_size=encoder_output_size, **ctc_conf
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)
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if predictor is not None:
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predictor_class = predictor_choices.get_class(predictor)
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predictor = predictor_class(**predictor_conf)
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# note that eos is the same as sos (equivalent ID)
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self.blank_id = blank_id
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self.sos = sos if sos is not None else vocab_size - 1
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self.eos = eos if eos is not None else vocab_size - 1
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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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#
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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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#
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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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#
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# self.error_calculator = None
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#
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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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#
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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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#
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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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#
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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.predictor_bias = predictor_bias
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self.sampling_ratio = sampling_ratio
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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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#
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self.share_embedding = share_embedding
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if self.share_embedding:
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self.decoder.embed = None
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self.use_1st_decoder_loss = use_1st_decoder_loss
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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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**kwargs,
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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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decoding_ind: int
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"""
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decoding_ind = kwargs.get("kwargs", None)
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# import pdb;
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# pdb.set_trace()
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if len(text_lengths.size()) > 1:
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text_lengths = text_lengths[:, 0]
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if len(speech_lengths.size()) > 1:
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speech_lengths = speech_lengths[:, 0]
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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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# 1. Encoder
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if hasattr(self.encoder, "overlap_chunk_cls"):
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ind = self.encoder.overlap_chunk_cls.random_choice(self.training, decoding_ind)
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encoder_out, encoder_out_lens = self.encode(speech, speech_lengths, ind=ind)
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else:
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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, pre_loss_att, acc_att, cer_att, wer_att = None, 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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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 = (
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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, pre_loss_att = self._calc_att_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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if self.use_1st_decoder_loss and pre_loss_att is not None:
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loss = loss + (1 - self.ctc_weight) * pre_loss_att
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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["pre_loss_att"] = pre_loss_att.detach() if pre_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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if self.length_normalized_loss:
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batch_size = (text_lengths + self.predictor_bias).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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ind: int
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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(speech, speech_lengths)
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# 3. Normalization for feature: e.g. Global-CMVN, Utterance-CMVN
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if self.normalize is not None:
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feats, feats_lengths = self.normalize(feats, feats_lengths)
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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)
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if self.encoder.interctc_use_conditioning:
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if hasattr(self.encoder, "overlap_chunk_cls"):
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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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encoder_out, encoder_out_lens = 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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else:
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encoder_out, encoder_out_lens, _ = self.encoder(
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feats, feats_lengths, ctc=self.ctc
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)
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else:
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if hasattr(self.encoder, "overlap_chunk_cls"):
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encoder_out, encoder_out_lens, _ = self.encoder(feats, feats_lengths, ind=ind)
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encoder_out, encoder_out_lens = 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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else:
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encoder_out, encoder_out_lens, _ = self.encoder(feats, feats_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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# # 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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# )
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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 encoder_out, encoder_out_lens
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def calc_predictor(self, encoder_out, encoder_out_lens):
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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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pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = self.predictor(encoder_out, None, encoder_out_mask,
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ignore_id=self.ignore_id)
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return pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index
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def cal_decoder_with_predictor(self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens):
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decoder_outs = self.decoder(
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encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens
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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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def _extract_feats(
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self, speech: torch.Tensor, speech_lengths: torch.Tensor
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) -> Tuple[torch.Tensor, torch.Tensor]:
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assert speech_lengths.dim() == 1, speech_lengths.shape
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# for data-parallel
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speech = speech[:, : speech_lengths.max()]
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if self.frontend is not None:
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# Frontend
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# e.g. STFT and Feature extract
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# data_loader may send time-domain signal in this case
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# speech (Batch, NSamples) -> feats: (Batch, NFrames, Dim)
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feats, feats_lengths = self.frontend(speech, speech_lengths)
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else:
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# No frontend and no feature extract
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feats, feats_lengths = speech, speech_lengths
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return feats, feats_lengths
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def nll(
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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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) -> torch.Tensor:
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"""Compute negative log likelihood(nll) from transformer-decoder
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Normally, this function is called in batchify_nll.
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Args:
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encoder_out: (Batch, Length, Dim)
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encoder_out_lens: (Batch,)
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ys_pad: (Batch, Length)
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ys_pad_lens: (Batch,)
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"""
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ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
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ys_in_lens = ys_pad_lens + 1
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# 1. Forward decoder
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decoder_out, _ = self.decoder(
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encoder_out, encoder_out_lens, ys_in_pad, ys_in_lens
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) # [batch, seqlen, dim]
|
|
batch_size = decoder_out.size(0)
|
|
decoder_num_class = decoder_out.size(2)
|
|
# nll: negative log-likelihood
|
|
nll = torch.nn.functional.cross_entropy(
|
|
decoder_out.view(-1, decoder_num_class),
|
|
ys_out_pad.view(-1),
|
|
ignore_index=self.ignore_id,
|
|
reduction="none",
|
|
)
|
|
nll = nll.view(batch_size, -1)
|
|
nll = nll.sum(dim=1)
|
|
assert nll.size(0) == batch_size
|
|
return nll
|
|
|
|
def batchify_nll(
|
|
self,
|
|
encoder_out: torch.Tensor,
|
|
encoder_out_lens: torch.Tensor,
|
|
ys_pad: torch.Tensor,
|
|
ys_pad_lens: torch.Tensor,
|
|
batch_size: int = 100,
|
|
):
|
|
"""Compute negative log likelihood(nll) from transformer-decoder
|
|
To avoid OOM, this fuction seperate the input into batches.
|
|
Then call nll for each batch and combine and return results.
|
|
Args:
|
|
encoder_out: (Batch, Length, Dim)
|
|
encoder_out_lens: (Batch,)
|
|
ys_pad: (Batch, Length)
|
|
ys_pad_lens: (Batch,)
|
|
batch_size: int, samples each batch contain when computing nll,
|
|
you may change this to avoid OOM or increase
|
|
GPU memory usage
|
|
"""
|
|
total_num = encoder_out.size(0)
|
|
if total_num <= batch_size:
|
|
nll = self.nll(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens)
|
|
else:
|
|
nll = []
|
|
start_idx = 0
|
|
while True:
|
|
end_idx = min(start_idx + batch_size, total_num)
|
|
batch_encoder_out = encoder_out[start_idx:end_idx, :, :]
|
|
batch_encoder_out_lens = encoder_out_lens[start_idx:end_idx]
|
|
batch_ys_pad = ys_pad[start_idx:end_idx, :]
|
|
batch_ys_pad_lens = ys_pad_lens[start_idx:end_idx]
|
|
batch_nll = self.nll(
|
|
batch_encoder_out,
|
|
batch_encoder_out_lens,
|
|
batch_ys_pad,
|
|
batch_ys_pad_lens,
|
|
)
|
|
nll.append(batch_nll)
|
|
start_idx = end_idx
|
|
if start_idx == total_num:
|
|
break
|
|
nll = torch.cat(nll)
|
|
assert nll.size(0) == total_num
|
|
return nll
|
|
|
|
def _calc_att_loss(
|
|
self,
|
|
encoder_out: torch.Tensor,
|
|
encoder_out_lens: torch.Tensor,
|
|
ys_pad: torch.Tensor,
|
|
ys_pad_lens: torch.Tensor,
|
|
):
|
|
encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
|
|
encoder_out.device)
|
|
if self.predictor_bias == 1:
|
|
_, ys_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
|
|
ys_pad_lens = ys_pad_lens + self.predictor_bias
|
|
pre_acoustic_embeds, pre_token_length, _, pre_peak_index = self.predictor(encoder_out, ys_pad, encoder_out_mask,
|
|
ignore_id=self.ignore_id)
|
|
|
|
# 0. sampler
|
|
decoder_out_1st = None
|
|
pre_loss_att = None
|
|
if self.sampling_ratio > 0.0:
|
|
|
|
|
|
if self.use_1st_decoder_loss:
|
|
sematic_embeds, decoder_out_1st, pre_loss_att = self.sampler_with_grad(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens,
|
|
pre_acoustic_embeds)
|
|
else:
|
|
sematic_embeds, decoder_out_1st = self.sampler(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens,
|
|
pre_acoustic_embeds)
|
|
else:
|
|
if self.step_cur < 2:
|
|
logging.info("disable sampler in paraformer, sampling_ratio: {}".format(self.sampling_ratio))
|
|
sematic_embeds = pre_acoustic_embeds
|
|
|
|
# 1. Forward decoder
|
|
decoder_outs = self.decoder(
|
|
encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens
|
|
)
|
|
decoder_out, _ = decoder_outs[0], decoder_outs[1]
|
|
|
|
if decoder_out_1st is None:
|
|
decoder_out_1st = decoder_out
|
|
# 2. Compute attention loss
|
|
loss_att = self.criterion_att(decoder_out, ys_pad)
|
|
acc_att = th_accuracy(
|
|
decoder_out_1st.view(-1, self.vocab_size),
|
|
ys_pad,
|
|
ignore_label=self.ignore_id,
|
|
)
|
|
loss_pre = self.criterion_pre(ys_pad_lens.type_as(pre_token_length), pre_token_length)
|
|
|
|
# Compute cer/wer using attention-decoder
|
|
if self.training or self.error_calculator is None:
|
|
cer_att, wer_att = None, None
|
|
else:
|
|
ys_hat = decoder_out_1st.argmax(dim=-1)
|
|
cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu())
|
|
|
|
return loss_att, acc_att, cer_att, wer_att, loss_pre, pre_loss_att
|
|
|
|
def sampler(self, encoder_out, encoder_out_lens, ys_pad, ys_pad_lens, pre_acoustic_embeds):
|
|
|
|
tgt_mask = (~make_pad_mask(ys_pad_lens, maxlen=ys_pad_lens.max())[:, :, None]).to(ys_pad.device)
|
|
ys_pad_masked = ys_pad * tgt_mask[:, :, 0]
|
|
if self.share_embedding:
|
|
ys_pad_embed = self.decoder.output_layer.weight[ys_pad_masked]
|
|
else:
|
|
ys_pad_embed = self.decoder.embed(ys_pad_masked)
|
|
with torch.no_grad():
|
|
decoder_outs = self.decoder(
|
|
encoder_out, encoder_out_lens, pre_acoustic_embeds, ys_pad_lens
|
|
)
|
|
decoder_out, _ = decoder_outs[0], decoder_outs[1]
|
|
pred_tokens = decoder_out.argmax(-1)
|
|
nonpad_positions = ys_pad.ne(self.ignore_id)
|
|
seq_lens = (nonpad_positions).sum(1)
|
|
same_num = ((pred_tokens == ys_pad) & nonpad_positions).sum(1)
|
|
input_mask = torch.ones_like(nonpad_positions)
|
|
bsz, seq_len = ys_pad.size()
|
|
for li in range(bsz):
|
|
target_num = (((seq_lens[li] - same_num[li].sum()).float()) * self.sampling_ratio).long()
|
|
if target_num > 0:
|
|
input_mask[li].scatter_(dim=0, index=torch.randperm(seq_lens[li])[:target_num].to(input_mask.device), value=0)
|
|
input_mask = input_mask.eq(1)
|
|
input_mask = input_mask.masked_fill(~nonpad_positions, False)
|
|
input_mask_expand_dim = input_mask.unsqueeze(2).to(pre_acoustic_embeds.device)
|
|
|
|
sematic_embeds = pre_acoustic_embeds.masked_fill(~input_mask_expand_dim, 0) + ys_pad_embed.masked_fill(
|
|
input_mask_expand_dim, 0)
|
|
return sematic_embeds * tgt_mask, decoder_out * tgt_mask
|
|
|
|
def sampler_with_grad(self, encoder_out, encoder_out_lens, ys_pad, ys_pad_lens, pre_acoustic_embeds):
|
|
tgt_mask = (~make_pad_mask(ys_pad_lens, maxlen=ys_pad_lens.max())[:, :, None]).to(ys_pad.device)
|
|
ys_pad_masked = ys_pad * tgt_mask[:, :, 0]
|
|
if self.share_embedding:
|
|
ys_pad_embed = self.decoder.output_layer.weight[ys_pad_masked]
|
|
else:
|
|
ys_pad_embed = self.decoder.embed(ys_pad_masked)
|
|
decoder_outs = self.decoder(
|
|
encoder_out, encoder_out_lens, pre_acoustic_embeds, ys_pad_lens
|
|
)
|
|
pre_loss_att = self.criterion_att(decoder_outs[0], ys_pad)
|
|
decoder_out, _ = decoder_outs[0], decoder_outs[1]
|
|
pred_tokens = decoder_out.argmax(-1)
|
|
nonpad_positions = ys_pad.ne(self.ignore_id)
|
|
seq_lens = (nonpad_positions).sum(1)
|
|
same_num = ((pred_tokens == ys_pad) & nonpad_positions).sum(1)
|
|
input_mask = torch.ones_like(nonpad_positions)
|
|
bsz, seq_len = ys_pad.size()
|
|
for li in range(bsz):
|
|
target_num = (((seq_lens[li] - same_num[li].sum()).float()) * self.sampling_ratio).long()
|
|
if target_num > 0:
|
|
input_mask[li].scatter_(dim=0, index=torch.randperm(seq_lens[li])[:target_num].to(input_mask.device), value=0)
|
|
input_mask = input_mask.eq(1)
|
|
input_mask = input_mask.masked_fill(~nonpad_positions, False)
|
|
input_mask_expand_dim = input_mask.unsqueeze(2).to(pre_acoustic_embeds.device)
|
|
|
|
sematic_embeds = pre_acoustic_embeds.masked_fill(~input_mask_expand_dim, 0) + ys_pad_embed.masked_fill(
|
|
input_mask_expand_dim, 0)
|
|
|
|
return sematic_embeds * tgt_mask, decoder_out * tgt_mask, pre_loss_att
|
|
|
|
def _calc_ctc_loss(
|
|
self,
|
|
encoder_out: torch.Tensor,
|
|
encoder_out_lens: torch.Tensor,
|
|
ys_pad: torch.Tensor,
|
|
ys_pad_lens: torch.Tensor,
|
|
):
|
|
# Calc CTC loss
|
|
loss_ctc = self.ctc(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens)
|
|
|
|
# Calc CER using CTC
|
|
cer_ctc = None
|
|
if not self.training and self.error_calculator is not None:
|
|
ys_hat = self.ctc.argmax(encoder_out).data
|
|
cer_ctc = self.error_calculator(ys_hat.cpu(), ys_pad.cpu(), is_ctc=True)
|
|
return loss_ctc, cer_ctc
|