mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
1761 lines
66 KiB
Python
1761 lines
66 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 tempfile
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import codecs
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import requests
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import re
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import copy
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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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import time
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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.models.paraformer.search import Hypothesis
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from funasr.models.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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from funasr.datasets.fun_datasets.load_audio_extract_fbank import load_audio, extract_fbank
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from funasr.utils import postprocess_utils
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from funasr.fileio.datadir_writer import DatadirWriter
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from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard
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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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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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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.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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self.beam_search = None
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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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"""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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# 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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# Encoder
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encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
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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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# decoder: 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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# decoder: Attention decoder branch
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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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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["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 encode(
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self, speech: torch.Tensor, speech_lengths: torch.Tensor, **kwargs,
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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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# Data augmentation
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if self.specaug is not None and self.training:
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speech, speech_lengths = self.specaug(speech, speech_lengths)
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# Normalization for feature: e.g. Global-CMVN, Utterance-CMVN
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if self.normalize is not None:
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speech, speech_lengths = self.normalize(speech, speech_lengths)
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# Forward encoder
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encoder_out, encoder_out_lens, _ = self.encoder(speech, speech_lengths)
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if isinstance(encoder_out, tuple):
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encoder_out = encoder_out[0]
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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,
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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 _calc_att_loss(
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self,
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encoder_out: torch.Tensor,
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encoder_out_lens: torch.Tensor,
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ys_pad: torch.Tensor,
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ys_pad_lens: torch.Tensor,
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):
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encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
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encoder_out.device)
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if self.predictor_bias == 1:
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_, ys_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
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ys_pad_lens = ys_pad_lens + self.predictor_bias
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pre_acoustic_embeds, pre_token_length, _, pre_peak_index = self.predictor(encoder_out, ys_pad, encoder_out_mask,
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ignore_id=self.ignore_id)
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# 0. sampler
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decoder_out_1st = None
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pre_loss_att = None
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if self.sampling_ratio > 0.0:
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sematic_embeds, decoder_out_1st = self.sampler(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens,
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pre_acoustic_embeds)
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else:
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sematic_embeds = pre_acoustic_embeds
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# 1. Forward decoder
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decoder_outs = self.decoder(
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encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens
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)
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decoder_out, _ = decoder_outs[0], decoder_outs[1]
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if decoder_out_1st is None:
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decoder_out_1st = decoder_out
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# 2. Compute attention loss
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loss_att = self.criterion_att(decoder_out, ys_pad)
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acc_att = th_accuracy(
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decoder_out_1st.view(-1, self.vocab_size),
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ys_pad,
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ignore_label=self.ignore_id,
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)
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loss_pre = self.criterion_pre(ys_pad_lens.type_as(pre_token_length), pre_token_length)
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# Compute cer/wer using attention-decoder
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if self.training or self.error_calculator is None:
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cer_att, wer_att = None, None
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else:
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ys_hat = decoder_out_1st.argmax(dim=-1)
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cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu())
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return loss_att, acc_att, cer_att, wer_att, loss_pre, pre_loss_att
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def sampler(self, encoder_out, encoder_out_lens, ys_pad, ys_pad_lens, pre_acoustic_embeds):
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tgt_mask = (~make_pad_mask(ys_pad_lens, maxlen=ys_pad_lens.max())[:, :, None]).to(ys_pad.device)
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ys_pad_masked = ys_pad * tgt_mask[:, :, 0]
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if self.share_embedding:
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ys_pad_embed = self.decoder.output_layer.weight[ys_pad_masked]
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else:
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ys_pad_embed = self.decoder.embed(ys_pad_masked)
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with torch.no_grad():
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decoder_outs = self.decoder(
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encoder_out, encoder_out_lens, pre_acoustic_embeds, ys_pad_lens
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)
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decoder_out, _ = decoder_outs[0], decoder_outs[1]
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pred_tokens = decoder_out.argmax(-1)
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nonpad_positions = ys_pad.ne(self.ignore_id)
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seq_lens = (nonpad_positions).sum(1)
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same_num = ((pred_tokens == ys_pad) & nonpad_positions).sum(1)
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input_mask = torch.ones_like(nonpad_positions)
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bsz, seq_len = ys_pad.size()
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for li in range(bsz):
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target_num = (((seq_lens[li] - same_num[li].sum()).float()) * self.sampling_ratio).long()
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if target_num > 0:
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input_mask[li].scatter_(dim=0,
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index=torch.randperm(seq_lens[li])[:target_num].to(input_mask.device),
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value=0)
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input_mask = input_mask.eq(1)
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input_mask = input_mask.masked_fill(~nonpad_positions, False)
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input_mask_expand_dim = input_mask.unsqueeze(2).to(pre_acoustic_embeds.device)
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sematic_embeds = pre_acoustic_embeds.masked_fill(~input_mask_expand_dim, 0) + ys_pad_embed.masked_fill(
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input_mask_expand_dim, 0)
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return sematic_embeds * tgt_mask, decoder_out * tgt_mask
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def _calc_ctc_loss(
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self,
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encoder_out: torch.Tensor,
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encoder_out_lens: torch.Tensor,
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ys_pad: torch.Tensor,
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ys_pad_lens: torch.Tensor,
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):
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# Calc CTC loss
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loss_ctc = self.ctc(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens)
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# Calc CER using CTC
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cer_ctc = None
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if not self.training and self.error_calculator is not None:
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ys_hat = self.ctc.argmax(encoder_out).data
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cer_ctc = self.error_calculator(ys_hat.cpu(), ys_pad.cpu(), is_ctc=True)
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return loss_ctc, cer_ctc
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def init_beam_search(self,
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**kwargs,
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):
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from funasr.models.paraformer.search import BeamSearchPara
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from funasr.modules.scorers.ctc import CTCPrefixScorer
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from funasr.modules.scorers.length_bonus import LengthBonus
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# 1. Build ASR model
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scorers = {}
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if self.ctc != None:
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ctc = CTCPrefixScorer(ctc=self.ctc, eos=self.eos)
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scorers.update(
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ctc=ctc
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)
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token_list = kwargs.get("token_list")
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scorers.update(
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length_bonus=LengthBonus(len(token_list)),
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)
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|
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# 3. Build ngram model
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# ngram is not supported now
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ngram = None
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scorers["ngram"] = ngram
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|
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weights = dict(
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decoder=1.0 - kwargs.get("decoding_ctc_weight"),
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ctc=kwargs.get("decoding_ctc_weight", 0.0),
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lm=kwargs.get("lm_weight", 0.0),
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ngram=kwargs.get("ngram_weight", 0.0),
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length_bonus=kwargs.get("penalty", 0.0),
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)
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beam_search = BeamSearchPara(
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beam_size=kwargs.get("beam_size", 2),
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weights=weights,
|
|
scorers=scorers,
|
|
sos=self.sos,
|
|
eos=self.eos,
|
|
vocab_size=len(token_list),
|
|
token_list=token_list,
|
|
pre_beam_score_key=None if self.ctc_weight == 1.0 else "full",
|
|
)
|
|
# beam_search.to(device=kwargs.get("device", "cpu"), dtype=getattr(torch, kwargs.get("dtype", "float32"))).eval()
|
|
# for scorer in scorers.values():
|
|
# if isinstance(scorer, torch.nn.Module):
|
|
# scorer.to(device=kwargs.get("device", "cpu"), dtype=getattr(torch, kwargs.get("dtype", "float32"))).eval()
|
|
self.beam_search = beam_search
|
|
|
|
def generate(self,
|
|
data_in: list,
|
|
data_lengths: list=None,
|
|
key: list=None,
|
|
tokenizer=None,
|
|
**kwargs,
|
|
):
|
|
|
|
# init beamsearch
|
|
is_use_ctc = kwargs.get("decoding_ctc_weight", 0.0) > 0.00001 and self.ctc != None
|
|
is_use_lm = kwargs.get("lm_weight", 0.0) > 0.00001 and kwargs.get("lm_file", None) is not None
|
|
if self.beam_search is None and (is_use_lm or is_use_ctc):
|
|
logging.info("enable beam_search")
|
|
self.init_beam_search(**kwargs)
|
|
self.nbest = kwargs.get("nbest", 1)
|
|
|
|
meta_data = {}
|
|
# extract fbank feats
|
|
time1 = time.perf_counter()
|
|
audio_sample_list = load_audio(data_in, fs=self.frontend.fs, audio_fs=kwargs.get("fs", 16000))
|
|
time2 = time.perf_counter()
|
|
meta_data["load_data"] = f"{time2 - time1:0.3f}"
|
|
speech, speech_lengths = extract_fbank(audio_sample_list, date_type=kwargs.get("date_type", "sound"), frontend=self.frontend)
|
|
time3 = time.perf_counter()
|
|
meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
|
|
meta_data["batch_data_time"] = speech_lengths.sum().item() * self.frontend.frame_shift * self.frontend.lfr_n / 1000
|
|
|
|
speech.to(device=kwargs["device"]), speech_lengths.to(device=kwargs["device"])
|
|
|
|
# Encoder
|
|
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
|
|
if isinstance(encoder_out, tuple):
|
|
encoder_out = encoder_out[0]
|
|
|
|
# predictor
|
|
predictor_outs = self.calc_predictor(encoder_out, encoder_out_lens)
|
|
pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = predictor_outs[0], predictor_outs[1], \
|
|
predictor_outs[2], predictor_outs[3]
|
|
pre_token_length = pre_token_length.round().long()
|
|
if torch.max(pre_token_length) < 1:
|
|
return []
|
|
decoder_outs = self.cal_decoder_with_predictor(encoder_out, encoder_out_lens, pre_acoustic_embeds,
|
|
pre_token_length)
|
|
decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
|
|
|
|
|
|
results = []
|
|
b, n, d = decoder_out.size()
|
|
for i in range(b):
|
|
x = encoder_out[i, :encoder_out_lens[i], :]
|
|
am_scores = decoder_out[i, :pre_token_length[i], :]
|
|
if self.beam_search is not None:
|
|
nbest_hyps = self.beam_search(
|
|
x=x, am_scores=am_scores, maxlenratio=kwargs.get("maxlenratio", 0.0), minlenratio=kwargs.get("minlenratio", 0.0)
|
|
)
|
|
|
|
nbest_hyps = nbest_hyps[: self.nbest]
|
|
else:
|
|
|
|
yseq = am_scores.argmax(dim=-1)
|
|
score = am_scores.max(dim=-1)[0]
|
|
score = torch.sum(score, dim=-1)
|
|
# pad with mask tokens to ensure compatibility with sos/eos tokens
|
|
yseq = torch.tensor(
|
|
[self.sos] + yseq.tolist() + [self.eos], device=yseq.device
|
|
)
|
|
nbest_hyps = [Hypothesis(yseq=yseq, score=score)]
|
|
for nbest_idx, hyp in enumerate(nbest_hyps):
|
|
ibest_writer = None
|
|
if ibest_writer is None and kwargs.get("output_dir") is not None:
|
|
writer = DatadirWriter(kwargs.get("output_dir"))
|
|
ibest_writer = writer[f"{nbest_idx+1}best_recog"]
|
|
# remove sos/eos and get results
|
|
last_pos = -1
|
|
if isinstance(hyp.yseq, list):
|
|
token_int = hyp.yseq[1:last_pos]
|
|
else:
|
|
token_int = hyp.yseq[1:last_pos].tolist()
|
|
|
|
# remove blank symbol id, which is assumed to be 0
|
|
token_int = list(filter(lambda x: x != self.eos and x != self.sos and x != self.blank_id, token_int))
|
|
|
|
# Change integer-ids to tokens
|
|
token = tokenizer.ids2tokens(token_int)
|
|
text = tokenizer.tokens2text(token)
|
|
|
|
text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
|
|
result_i = {"key": key[i], "token": token, "text": text, "text_postprocessed": text_postprocessed}
|
|
results.append(result_i)
|
|
|
|
if ibest_writer is not None:
|
|
ibest_writer["token"][key[i]] = " ".join(token)
|
|
ibest_writer["text"][key[i]] = text
|
|
ibest_writer["text_postprocessed"][key[i]] = text_postprocessed
|
|
|
|
return results, meta_data
|
|
|
|
|
|
|
|
class BiCifParaformer(Paraformer):
|
|
"""
|
|
Author: Speech Lab of DAMO Academy, Alibaba Group
|
|
Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
|
|
https://arxiv.org/abs/2206.08317
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
*args,
|
|
**kwargs,
|
|
):
|
|
super().__init__(*args, **kwargs)
|
|
assert isinstance(self.predictor, CifPredictorV3), "BiCifParaformer should use CIFPredictorV3"
|
|
|
|
|
|
def _calc_pre2_loss(
|
|
self,
|
|
encoder_out: torch.Tensor,
|
|
encoder_out_lens: torch.Tensor,
|
|
ys_pad: torch.Tensor,
|
|
ys_pad_lens: torch.Tensor,
|
|
):
|
|
encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
|
|
encoder_out.device)
|
|
if self.predictor_bias == 1:
|
|
_, ys_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
|
|
ys_pad_lens = ys_pad_lens + self.predictor_bias
|
|
_, _, _, _, pre_token_length2 = self.predictor(encoder_out, ys_pad, encoder_out_mask, ignore_id=self.ignore_id)
|
|
|
|
# loss_pre = self.criterion_pre(ys_pad_lens.type_as(pre_token_length), pre_token_length)
|
|
loss_pre2 = self.criterion_pre(ys_pad_lens.type_as(pre_token_length2), pre_token_length2)
|
|
|
|
return loss_pre2
|
|
|
|
|
|
def _calc_att_loss(
|
|
self,
|
|
encoder_out: torch.Tensor,
|
|
encoder_out_lens: torch.Tensor,
|
|
ys_pad: torch.Tensor,
|
|
ys_pad_lens: torch.Tensor,
|
|
):
|
|
encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
|
|
encoder_out.device)
|
|
if self.predictor_bias == 1:
|
|
_, ys_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
|
|
ys_pad_lens = ys_pad_lens + self.predictor_bias
|
|
pre_acoustic_embeds, pre_token_length, _, pre_peak_index, _ = self.predictor(encoder_out, ys_pad,
|
|
encoder_out_mask,
|
|
ignore_id=self.ignore_id)
|
|
|
|
# 0. sampler
|
|
decoder_out_1st = None
|
|
if self.sampling_ratio > 0.0:
|
|
sematic_embeds, decoder_out_1st = self.sampler(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens,
|
|
pre_acoustic_embeds)
|
|
else:
|
|
sematic_embeds = pre_acoustic_embeds
|
|
|
|
# 1. Forward decoder
|
|
decoder_outs = self.decoder(
|
|
encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens
|
|
)
|
|
decoder_out, _ = decoder_outs[0], decoder_outs[1]
|
|
|
|
if decoder_out_1st is None:
|
|
decoder_out_1st = decoder_out
|
|
# 2. Compute attention loss
|
|
loss_att = self.criterion_att(decoder_out, ys_pad)
|
|
acc_att = th_accuracy(
|
|
decoder_out_1st.view(-1, self.vocab_size),
|
|
ys_pad,
|
|
ignore_label=self.ignore_id,
|
|
)
|
|
loss_pre = self.criterion_pre(ys_pad_lens.type_as(pre_token_length), pre_token_length)
|
|
|
|
# Compute cer/wer using attention-decoder
|
|
if self.training or self.error_calculator is None:
|
|
cer_att, wer_att = None, None
|
|
else:
|
|
ys_hat = decoder_out_1st.argmax(dim=-1)
|
|
cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu())
|
|
|
|
return loss_att, acc_att, cer_att, wer_att, loss_pre
|
|
|
|
|
|
def calc_predictor(self, encoder_out, encoder_out_lens):
|
|
encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
|
|
encoder_out.device)
|
|
pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index, pre_token_length2 = self.predictor(encoder_out,
|
|
None,
|
|
encoder_out_mask,
|
|
ignore_id=self.ignore_id)
|
|
return pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index
|
|
|
|
|
|
def calc_predictor_timestamp(self, encoder_out, encoder_out_lens, token_num):
|
|
encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
|
|
encoder_out.device)
|
|
ds_alphas, ds_cif_peak, us_alphas, us_peaks = self.predictor.get_upsample_timestamp(encoder_out,
|
|
encoder_out_mask,
|
|
token_num)
|
|
return ds_alphas, ds_cif_peak, us_alphas, us_peaks
|
|
|
|
|
|
def forward(
|
|
self,
|
|
speech: torch.Tensor,
|
|
speech_lengths: torch.Tensor,
|
|
text: torch.Tensor,
|
|
text_lengths: torch.Tensor,
|
|
**kwargs,
|
|
) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
|
|
"""Frontend + Encoder + Decoder + Calc loss
|
|
Args:
|
|
speech: (Batch, Length, ...)
|
|
speech_lengths: (Batch, )
|
|
text: (Batch, Length)
|
|
text_lengths: (Batch,)
|
|
"""
|
|
if len(text_lengths.size()) > 1:
|
|
text_lengths = text_lengths[:, 0]
|
|
if len(speech_lengths.size()) > 1:
|
|
speech_lengths = speech_lengths[:, 0]
|
|
|
|
batch_size = speech.shape[0]
|
|
|
|
# Encoder
|
|
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
|
|
|
|
|
|
loss_ctc, cer_ctc = None, None
|
|
loss_pre = None
|
|
stats = dict()
|
|
|
|
# decoder: CTC branch
|
|
if self.ctc_weight != 0.0:
|
|
loss_ctc, cer_ctc = self._calc_ctc_loss(
|
|
encoder_out, encoder_out_lens, text, text_lengths
|
|
)
|
|
|
|
# Collect CTC branch stats
|
|
stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None
|
|
stats["cer_ctc"] = cer_ctc
|
|
|
|
|
|
# decoder: Attention decoder branch
|
|
loss_att, acc_att, cer_att, wer_att, loss_pre = self._calc_att_loss(
|
|
encoder_out, encoder_out_lens, text, text_lengths
|
|
)
|
|
|
|
loss_pre2 = self._calc_pre2_loss(
|
|
encoder_out, encoder_out_lens, text, text_lengths
|
|
)
|
|
|
|
# 3. CTC-Att loss definition
|
|
if self.ctc_weight == 0.0:
|
|
loss = loss_att + loss_pre * self.predictor_weight + loss_pre2 * self.predictor_weight * 0.5
|
|
else:
|
|
loss = self.ctc_weight * loss_ctc + (
|
|
1 - self.ctc_weight) * loss_att + loss_pre * self.predictor_weight + loss_pre2 * self.predictor_weight * 0.5
|
|
|
|
# Collect Attn branch stats
|
|
stats["loss_att"] = loss_att.detach() if loss_att is not None else None
|
|
stats["acc"] = acc_att
|
|
stats["cer"] = cer_att
|
|
stats["wer"] = wer_att
|
|
stats["loss_pre"] = loss_pre.detach().cpu() if loss_pre is not None else None
|
|
stats["loss_pre2"] = loss_pre2.detach().cpu()
|
|
|
|
stats["loss"] = torch.clone(loss.detach())
|
|
|
|
# force_gatherable: to-device and to-tensor if scalar for DataParallel
|
|
if self.length_normalized_loss:
|
|
batch_size = int((text_lengths + self.predictor_bias).sum())
|
|
|
|
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
|
|
return loss, stats, weight
|
|
|
|
def generate(self,
|
|
data_in: list,
|
|
data_lengths: list = None,
|
|
key: list = None,
|
|
tokenizer=None,
|
|
**kwargs,
|
|
):
|
|
|
|
# init beamsearch
|
|
is_use_ctc = kwargs.get("decoding_ctc_weight", 0.0) > 0.00001 and self.ctc != None
|
|
is_use_lm = kwargs.get("lm_weight", 0.0) > 0.00001 and kwargs.get("lm_file", None) is not None
|
|
if self.beam_search is None and (is_use_lm or is_use_ctc):
|
|
logging.info("enable beam_search")
|
|
self.init_beam_search(**kwargs)
|
|
self.nbest = kwargs.get("nbest", 1)
|
|
|
|
meta_data = {}
|
|
# extract fbank feats
|
|
time1 = time.perf_counter()
|
|
audio_sample_list = load_audio(data_in, fs=self.frontend.fs, audio_fs=kwargs.get("fs", 16000))
|
|
time2 = time.perf_counter()
|
|
meta_data["load_data"] = f"{time2 - time1:0.3f}"
|
|
speech, speech_lengths = extract_fbank(audio_sample_list, date_type=kwargs.get("date_type", "sound"),
|
|
frontend=self.frontend)
|
|
time3 = time.perf_counter()
|
|
meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
|
|
meta_data[
|
|
"batch_data_time"] = speech_lengths.sum().item() * self.frontend.frame_shift * self.frontend.lfr_n / 1000
|
|
|
|
speech.to(device=kwargs["device"]), speech_lengths.to(device=kwargs["device"])
|
|
|
|
# Encoder
|
|
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
|
|
if isinstance(encoder_out, tuple):
|
|
encoder_out = encoder_out[0]
|
|
|
|
# predictor
|
|
predictor_outs = self.calc_predictor(encoder_out, encoder_out_lens)
|
|
pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = predictor_outs[0], predictor_outs[1], \
|
|
predictor_outs[2], predictor_outs[3]
|
|
pre_token_length = pre_token_length.round().long()
|
|
if torch.max(pre_token_length) < 1:
|
|
return []
|
|
decoder_outs = self.cal_decoder_with_predictor(encoder_out, encoder_out_lens, pre_acoustic_embeds,
|
|
pre_token_length)
|
|
decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
|
|
|
|
# BiCifParaformer, test no bias cif2
|
|
|
|
_, _, us_alphas, us_peaks = self.calc_predictor_timestamp(encoder_out, encoder_out_lens,
|
|
pre_token_length)
|
|
|
|
results = []
|
|
b, n, d = decoder_out.size()
|
|
for i in range(b):
|
|
x = encoder_out[i, :encoder_out_lens[i], :]
|
|
am_scores = decoder_out[i, :pre_token_length[i], :]
|
|
if self.beam_search is not None:
|
|
nbest_hyps = self.beam_search(
|
|
x=x, am_scores=am_scores, maxlenratio=kwargs.get("maxlenratio", 0.0),
|
|
minlenratio=kwargs.get("minlenratio", 0.0)
|
|
)
|
|
|
|
nbest_hyps = nbest_hyps[: self.nbest]
|
|
else:
|
|
|
|
yseq = am_scores.argmax(dim=-1)
|
|
score = am_scores.max(dim=-1)[0]
|
|
score = torch.sum(score, dim=-1)
|
|
# pad with mask tokens to ensure compatibility with sos/eos tokens
|
|
yseq = torch.tensor(
|
|
[self.sos] + yseq.tolist() + [self.eos], device=yseq.device
|
|
)
|
|
nbest_hyps = [Hypothesis(yseq=yseq, score=score)]
|
|
for nbest_idx, hyp in enumerate(nbest_hyps):
|
|
ibest_writer = None
|
|
if ibest_writer is None and kwargs.get("output_dir") is not None:
|
|
writer = DatadirWriter(kwargs.get("output_dir"))
|
|
ibest_writer = writer[f"{nbest_idx + 1}best_recog"]
|
|
# remove sos/eos and get results
|
|
last_pos = -1
|
|
if isinstance(hyp.yseq, list):
|
|
token_int = hyp.yseq[1:last_pos]
|
|
else:
|
|
token_int = hyp.yseq[1:last_pos].tolist()
|
|
|
|
# remove blank symbol id, which is assumed to be 0
|
|
token_int = list(filter(lambda x: x != self.eos and x != self.sos and x != self.blank_id, token_int))
|
|
|
|
# Change integer-ids to tokens
|
|
token = tokenizer.ids2tokens(token_int)
|
|
text = tokenizer.tokens2text(token)
|
|
|
|
_, timestamp = ts_prediction_lfr6_standard(us_alphas[i][:encoder_out_lens[i] * 3],
|
|
us_peaks[i][:encoder_out_lens[i] * 3],
|
|
copy.copy(token),
|
|
vad_offset=kwargs.get("begin_time", 0))
|
|
|
|
text_postprocessed, time_stamp_postprocessed, word_lists = postprocess_utils.sentence_postprocess(token, timestamp)
|
|
|
|
result_i = {"key": key[i], "token": token, "text": text, "text_postprocessed": text_postprocessed,
|
|
"time_stamp_postprocessed": time_stamp_postprocessed,
|
|
"word_lists": word_lists
|
|
}
|
|
results.append(result_i)
|
|
|
|
if ibest_writer is not None:
|
|
ibest_writer["token"][key[i]] = " ".join(token)
|
|
ibest_writer["text"][key[i]] = text
|
|
ibest_writer["text_postprocessed"][key[i]] = text_postprocessed
|
|
|
|
|
|
return results, meta_data
|
|
|
|
|
|
class NeatContextualParaformer(Paraformer):
|
|
"""
|
|
Author: Speech Lab of DAMO Academy, Alibaba Group
|
|
Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
|
|
https://arxiv.org/abs/2206.08317
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
*args,
|
|
**kwargs,
|
|
):
|
|
super().__init__(*args, **kwargs)
|
|
|
|
self.target_buffer_length = kwargs.get("target_buffer_length", -1)
|
|
inner_dim = kwargs.get("inner_dim", 256)
|
|
bias_encoder_type = kwargs.get("bias_encoder_type", "lstm")
|
|
use_decoder_embedding = kwargs.get("use_decoder_embedding", False)
|
|
crit_attn_weight = kwargs.get("crit_attn_weight", 0.0)
|
|
crit_attn_smooth = kwargs.get("crit_attn_smooth", 0.0)
|
|
bias_encoder_dropout_rate = kwargs.get("bias_encoder_dropout_rate", 0.0)
|
|
|
|
|
|
if bias_encoder_type == 'lstm':
|
|
logging.warning("enable bias encoder sampling and contextual training")
|
|
self.bias_encoder = torch.nn.LSTM(inner_dim, inner_dim, 1, batch_first=True, dropout=bias_encoder_dropout_rate)
|
|
self.bias_embed = torch.nn.Embedding(self.vocab_size, inner_dim)
|
|
elif bias_encoder_type == 'mean':
|
|
logging.warning("enable bias encoder sampling and contextual training")
|
|
self.bias_embed = torch.nn.Embedding(self.vocab_size, inner_dim)
|
|
else:
|
|
logging.error("Unsupport bias encoder type: {}".format(bias_encoder_type))
|
|
|
|
if self.target_buffer_length > 0:
|
|
self.hotword_buffer = None
|
|
self.length_record = []
|
|
self.current_buffer_length = 0
|
|
self.use_decoder_embedding = use_decoder_embedding
|
|
self.crit_attn_weight = crit_attn_weight
|
|
if self.crit_attn_weight > 0:
|
|
self.attn_loss = torch.nn.L1Loss()
|
|
self.crit_attn_smooth = crit_attn_smooth
|
|
|
|
|
|
def forward(
|
|
self,
|
|
speech: torch.Tensor,
|
|
speech_lengths: torch.Tensor,
|
|
text: torch.Tensor,
|
|
text_lengths: torch.Tensor,
|
|
hotword_pad: torch.Tensor,
|
|
hotword_lengths: torch.Tensor,
|
|
dha_pad: torch.Tensor,
|
|
**kwargs,
|
|
) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
|
|
"""Frontend + Encoder + Decoder + Calc loss
|
|
|
|
Args:
|
|
speech: (Batch, Length, ...)
|
|
speech_lengths: (Batch, )
|
|
text: (Batch, Length)
|
|
text_lengths: (Batch,)
|
|
"""
|
|
if len(text_lengths.size()) > 1:
|
|
text_lengths = text_lengths[:, 0]
|
|
if len(speech_lengths.size()) > 1:
|
|
speech_lengths = speech_lengths[:, 0]
|
|
|
|
batch_size = speech.shape[0]
|
|
|
|
# 1. Encoder
|
|
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
|
|
|
|
|
|
loss_ctc, cer_ctc = None, None
|
|
|
|
stats = dict()
|
|
|
|
# 1. CTC branch
|
|
if self.ctc_weight != 0.0:
|
|
loss_ctc, cer_ctc = self._calc_ctc_loss(
|
|
encoder_out, encoder_out_lens, text, text_lengths
|
|
)
|
|
|
|
# Collect CTC branch stats
|
|
stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None
|
|
stats["cer_ctc"] = cer_ctc
|
|
|
|
|
|
# 2b. Attention decoder branch
|
|
loss_att, acc_att, cer_att, wer_att, loss_pre, loss_ideal = self._calc_att_clas_loss(
|
|
encoder_out, encoder_out_lens, text, text_lengths, hotword_pad, hotword_lengths
|
|
)
|
|
|
|
# 3. CTC-Att loss definition
|
|
if self.ctc_weight == 0.0:
|
|
loss = loss_att + loss_pre * self.predictor_weight
|
|
else:
|
|
loss = self.ctc_weight * loss_ctc + (1 - self.ctc_weight) * loss_att + loss_pre * self.predictor_weight
|
|
|
|
if loss_ideal is not None:
|
|
loss = loss + loss_ideal * self.crit_attn_weight
|
|
stats["loss_ideal"] = loss_ideal.detach().cpu()
|
|
|
|
# Collect Attn branch stats
|
|
stats["loss_att"] = loss_att.detach() if loss_att is not None else None
|
|
stats["acc"] = acc_att
|
|
stats["cer"] = cer_att
|
|
stats["wer"] = wer_att
|
|
stats["loss_pre"] = loss_pre.detach().cpu() if loss_pre is not None else None
|
|
|
|
stats["loss"] = torch.clone(loss.detach())
|
|
# force_gatherable: to-device and to-tensor if scalar for DataParallel
|
|
if self.length_normalized_loss:
|
|
batch_size = int((text_lengths + self.predictor_bias).sum())
|
|
|
|
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
|
|
return loss, stats, weight
|
|
|
|
|
|
def _calc_att_clas_loss(
|
|
self,
|
|
encoder_out: torch.Tensor,
|
|
encoder_out_lens: torch.Tensor,
|
|
ys_pad: torch.Tensor,
|
|
ys_pad_lens: torch.Tensor,
|
|
hotword_pad: torch.Tensor,
|
|
hotword_lengths: 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, _, _ = self.predictor(encoder_out, ys_pad, encoder_out_mask,
|
|
ignore_id=self.ignore_id)
|
|
|
|
# -1. bias encoder
|
|
if self.use_decoder_embedding:
|
|
hw_embed = self.decoder.embed(hotword_pad)
|
|
else:
|
|
hw_embed = self.bias_embed(hotword_pad)
|
|
hw_embed, (_, _) = self.bias_encoder(hw_embed)
|
|
_ind = np.arange(0, hotword_pad.shape[0]).tolist()
|
|
selected = hw_embed[_ind, [i - 1 for i in hotword_lengths.detach().cpu().tolist()]]
|
|
contextual_info = selected.squeeze(0).repeat(ys_pad.shape[0], 1, 1).to(ys_pad.device)
|
|
|
|
# 0. sampler
|
|
decoder_out_1st = None
|
|
if self.sampling_ratio > 0.0:
|
|
if self.step_cur < 2:
|
|
logging.info("enable sampler in paraformer, sampling_ratio: {}".format(self.sampling_ratio))
|
|
sematic_embeds, decoder_out_1st = self.sampler(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens,
|
|
pre_acoustic_embeds, contextual_info)
|
|
else:
|
|
if self.step_cur < 2:
|
|
logging.info("disable sampler in paraformer, sampling_ratio: {}".format(self.sampling_ratio))
|
|
sematic_embeds = pre_acoustic_embeds
|
|
|
|
# 1. Forward decoder
|
|
decoder_outs = self.decoder(
|
|
encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, contextual_info=contextual_info
|
|
)
|
|
decoder_out, _ = decoder_outs[0], decoder_outs[1]
|
|
'''
|
|
if self.crit_attn_weight > 0 and attn.shape[-1] > 1:
|
|
ideal_attn = ideal_attn + self.crit_attn_smooth / (self.crit_attn_smooth + 1.0)
|
|
attn_non_blank = attn[:,:,:,:-1]
|
|
ideal_attn_non_blank = ideal_attn[:,:,:-1]
|
|
loss_ideal = self.attn_loss(attn_non_blank.max(1)[0], ideal_attn_non_blank.to(attn.device))
|
|
else:
|
|
loss_ideal = None
|
|
'''
|
|
loss_ideal = None
|
|
|
|
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, loss_ideal
|
|
|
|
|
|
def sampler(self, encoder_out, encoder_out_lens, ys_pad, ys_pad_lens, pre_acoustic_embeds, contextual_info):
|
|
tgt_mask = (~make_pad_mask(ys_pad_lens, maxlen=ys_pad_lens.max())[:, :, None]).to(ys_pad.device)
|
|
ys_pad = ys_pad * tgt_mask[:, :, 0]
|
|
if self.share_embedding:
|
|
ys_pad_embed = self.decoder.output_layer.weight[ys_pad]
|
|
else:
|
|
ys_pad_embed = self.decoder.embed(ys_pad)
|
|
with torch.no_grad():
|
|
decoder_outs = self.decoder(
|
|
encoder_out, encoder_out_lens, pre_acoustic_embeds, ys_pad_lens, contextual_info=contextual_info
|
|
)
|
|
decoder_out, _ = decoder_outs[0], decoder_outs[1]
|
|
pred_tokens = decoder_out.argmax(-1)
|
|
nonpad_positions = ys_pad.ne(self.ignore_id)
|
|
seq_lens = (nonpad_positions).sum(1)
|
|
same_num = ((pred_tokens == ys_pad) & nonpad_positions).sum(1)
|
|
input_mask = torch.ones_like(nonpad_positions)
|
|
bsz, seq_len = ys_pad.size()
|
|
for li in range(bsz):
|
|
target_num = (((seq_lens[li] - same_num[li].sum()).float()) * self.sampling_ratio).long()
|
|
if target_num > 0:
|
|
input_mask[li].scatter_(dim=0,
|
|
index=torch.randperm(seq_lens[li])[:target_num].to(pre_acoustic_embeds.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 cal_decoder_with_predictor(self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, hw_list=None,
|
|
clas_scale=1.0):
|
|
if hw_list is None:
|
|
hw_list = [torch.Tensor([1]).long().to(encoder_out.device)] # empty hotword list
|
|
hw_list_pad = pad_list(hw_list, 0)
|
|
if self.use_decoder_embedding:
|
|
hw_embed = self.decoder.embed(hw_list_pad)
|
|
else:
|
|
hw_embed = self.bias_embed(hw_list_pad)
|
|
hw_embed, (h_n, _) = self.bias_encoder(hw_embed)
|
|
hw_embed = h_n.repeat(encoder_out.shape[0], 1, 1)
|
|
else:
|
|
hw_lengths = [len(i) for i in hw_list]
|
|
hw_list_pad = pad_list([torch.Tensor(i).long() for i in hw_list], 0).to(encoder_out.device)
|
|
if self.use_decoder_embedding:
|
|
hw_embed = self.decoder.embed(hw_list_pad)
|
|
else:
|
|
hw_embed = self.bias_embed(hw_list_pad)
|
|
hw_embed = torch.nn.utils.rnn.pack_padded_sequence(hw_embed, hw_lengths, batch_first=True,
|
|
enforce_sorted=False)
|
|
_, (h_n, _) = self.bias_encoder(hw_embed)
|
|
hw_embed = h_n.repeat(encoder_out.shape[0], 1, 1)
|
|
|
|
decoder_outs = self.decoder(
|
|
encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, contextual_info=hw_embed, clas_scale=clas_scale
|
|
)
|
|
decoder_out = decoder_outs[0]
|
|
decoder_out = torch.log_softmax(decoder_out, dim=-1)
|
|
return decoder_out, ys_pad_lens
|
|
|
|
def generate(self,
|
|
data_in: list,
|
|
data_lengths: list = None,
|
|
key: list = None,
|
|
tokenizer=None,
|
|
**kwargs,
|
|
):
|
|
|
|
# init beamsearch
|
|
is_use_ctc = kwargs.get("decoding_ctc_weight", 0.0) > 0.00001 and self.ctc != None
|
|
is_use_lm = kwargs.get("lm_weight", 0.0) > 0.00001 and kwargs.get("lm_file", None) is not None
|
|
if self.beam_search is None and (is_use_lm or is_use_ctc):
|
|
logging.info("enable beam_search")
|
|
self.init_beam_search(**kwargs)
|
|
self.nbest = kwargs.get("nbest", 1)
|
|
|
|
meta_data = {}
|
|
|
|
# extract fbank feats
|
|
time1 = time.perf_counter()
|
|
audio_sample_list = load_audio(data_in, fs=self.frontend.fs, audio_fs=kwargs.get("fs", 16000))
|
|
time2 = time.perf_counter()
|
|
meta_data["load_data"] = f"{time2 - time1:0.3f}"
|
|
speech, speech_lengths = extract_fbank(audio_sample_list, date_type=kwargs.get("date_type", "sound"),
|
|
frontend=self.frontend)
|
|
time3 = time.perf_counter()
|
|
meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
|
|
meta_data[
|
|
"batch_data_time"] = speech_lengths.sum().item() * self.frontend.frame_shift * self.frontend.lfr_n / 1000
|
|
|
|
speech.to(device=kwargs["device"]), speech_lengths.to(device=kwargs["device"])
|
|
|
|
# hotword
|
|
self.hotword_list = self.generate_hotwords_list(kwargs.get("hotword", None), tokenizer=tokenizer)
|
|
|
|
# Encoder
|
|
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
|
|
if isinstance(encoder_out, tuple):
|
|
encoder_out = encoder_out[0]
|
|
|
|
# predictor
|
|
predictor_outs = self.calc_predictor(encoder_out, encoder_out_lens)
|
|
pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = predictor_outs[0], predictor_outs[1], \
|
|
predictor_outs[2], predictor_outs[3]
|
|
pre_token_length = pre_token_length.round().long()
|
|
if torch.max(pre_token_length) < 1:
|
|
return []
|
|
|
|
|
|
decoder_outs = self.cal_decoder_with_predictor(encoder_out, encoder_out_lens,
|
|
pre_acoustic_embeds,
|
|
pre_token_length,
|
|
hw_list=self.hotword_list,
|
|
clas_scale=kwargs.get("clas_scale", 1.0))
|
|
decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
|
|
|
|
results = []
|
|
b, n, d = decoder_out.size()
|
|
for i in range(b):
|
|
x = encoder_out[i, :encoder_out_lens[i], :]
|
|
am_scores = decoder_out[i, :pre_token_length[i], :]
|
|
if self.beam_search is not None:
|
|
nbest_hyps = self.beam_search(
|
|
x=x, am_scores=am_scores, maxlenratio=kwargs.get("maxlenratio", 0.0),
|
|
minlenratio=kwargs.get("minlenratio", 0.0)
|
|
)
|
|
|
|
nbest_hyps = nbest_hyps[: self.nbest]
|
|
else:
|
|
|
|
yseq = am_scores.argmax(dim=-1)
|
|
score = am_scores.max(dim=-1)[0]
|
|
score = torch.sum(score, dim=-1)
|
|
# pad with mask tokens to ensure compatibility with sos/eos tokens
|
|
yseq = torch.tensor(
|
|
[self.sos] + yseq.tolist() + [self.eos], device=yseq.device
|
|
)
|
|
nbest_hyps = [Hypothesis(yseq=yseq, score=score)]
|
|
for nbest_idx, hyp in enumerate(nbest_hyps):
|
|
ibest_writer = None
|
|
if ibest_writer is None and kwargs.get("output_dir") is not None:
|
|
writer = DatadirWriter(kwargs.get("output_dir"))
|
|
ibest_writer = writer[f"{nbest_idx + 1}best_recog"]
|
|
# remove sos/eos and get results
|
|
last_pos = -1
|
|
if isinstance(hyp.yseq, list):
|
|
token_int = hyp.yseq[1:last_pos]
|
|
else:
|
|
token_int = hyp.yseq[1:last_pos].tolist()
|
|
|
|
# remove blank symbol id, which is assumed to be 0
|
|
token_int = list(
|
|
filter(lambda x: x != self.eos and x != self.sos and x != self.blank_id, token_int))
|
|
|
|
# Change integer-ids to tokens
|
|
token = tokenizer.ids2tokens(token_int)
|
|
text = tokenizer.tokens2text(token)
|
|
|
|
text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
|
|
result_i = {"key": key[i], "token": token, "text": text, "text_postprocessed": text_postprocessed}
|
|
results.append(result_i)
|
|
|
|
if ibest_writer is not None:
|
|
ibest_writer["token"][key[i]] = " ".join(token)
|
|
ibest_writer["text"][key[i]] = text
|
|
ibest_writer["text_postprocessed"][key[i]] = text_postprocessed
|
|
|
|
return results, meta_data
|
|
|
|
|
|
def generate_hotwords_list(self, hotword_list_or_file, tokenizer=None):
|
|
def load_seg_dict(seg_dict_file):
|
|
seg_dict = {}
|
|
assert isinstance(seg_dict_file, str)
|
|
with open(seg_dict_file, "r", encoding="utf8") as f:
|
|
lines = f.readlines()
|
|
for line in lines:
|
|
s = line.strip().split()
|
|
key = s[0]
|
|
value = s[1:]
|
|
seg_dict[key] = " ".join(value)
|
|
return seg_dict
|
|
|
|
def seg_tokenize(txt, seg_dict):
|
|
pattern = re.compile(r'^[\u4E00-\u9FA50-9]+$')
|
|
out_txt = ""
|
|
for word in txt:
|
|
word = word.lower()
|
|
if word in seg_dict:
|
|
out_txt += seg_dict[word] + " "
|
|
else:
|
|
if pattern.match(word):
|
|
for char in word:
|
|
if char in seg_dict:
|
|
out_txt += seg_dict[char] + " "
|
|
else:
|
|
out_txt += "<unk>" + " "
|
|
else:
|
|
out_txt += "<unk>" + " "
|
|
return out_txt.strip().split()
|
|
|
|
seg_dict = None
|
|
if self.frontend.cmvn_file is not None:
|
|
model_dir = os.path.dirname(self.frontend.cmvn_file)
|
|
seg_dict_file = os.path.join(model_dir, 'seg_dict')
|
|
if os.path.exists(seg_dict_file):
|
|
seg_dict = load_seg_dict(seg_dict_file)
|
|
else:
|
|
seg_dict = None
|
|
# for None
|
|
if hotword_list_or_file is None:
|
|
hotword_list = None
|
|
# for local txt inputs
|
|
elif os.path.exists(hotword_list_or_file) and hotword_list_or_file.endswith('.txt'):
|
|
logging.info("Attempting to parse hotwords from local txt...")
|
|
hotword_list = []
|
|
hotword_str_list = []
|
|
with codecs.open(hotword_list_or_file, 'r') as fin:
|
|
for line in fin.readlines():
|
|
hw = line.strip()
|
|
hw_list = hw.split()
|
|
if seg_dict is not None:
|
|
hw_list = seg_tokenize(hw_list, seg_dict)
|
|
hotword_str_list.append(hw)
|
|
hotword_list.append(tokenizer.tokens2ids(hw_list))
|
|
hotword_list.append([self.sos])
|
|
hotword_str_list.append('<s>')
|
|
logging.info("Initialized hotword list from file: {}, hotword list: {}."
|
|
.format(hotword_list_or_file, hotword_str_list))
|
|
# for url, download and generate txt
|
|
elif hotword_list_or_file.startswith('http'):
|
|
logging.info("Attempting to parse hotwords from url...")
|
|
work_dir = tempfile.TemporaryDirectory().name
|
|
if not os.path.exists(work_dir):
|
|
os.makedirs(work_dir)
|
|
text_file_path = os.path.join(work_dir, os.path.basename(hotword_list_or_file))
|
|
local_file = requests.get(hotword_list_or_file)
|
|
open(text_file_path, "wb").write(local_file.content)
|
|
hotword_list_or_file = text_file_path
|
|
hotword_list = []
|
|
hotword_str_list = []
|
|
with codecs.open(hotword_list_or_file, 'r') as fin:
|
|
for line in fin.readlines():
|
|
hw = line.strip()
|
|
hw_list = hw.split()
|
|
if seg_dict is not None:
|
|
hw_list = seg_tokenize(hw_list, seg_dict)
|
|
hotword_str_list.append(hw)
|
|
hotword_list.append(tokenizer.tokens2ids(hw_list))
|
|
hotword_list.append([self.sos])
|
|
hotword_str_list.append('<s>')
|
|
logging.info("Initialized hotword list from file: {}, hotword list: {}."
|
|
.format(hotword_list_or_file, hotword_str_list))
|
|
# for text str input
|
|
elif not hotword_list_or_file.endswith('.txt'):
|
|
logging.info("Attempting to parse hotwords as str...")
|
|
hotword_list = []
|
|
hotword_str_list = []
|
|
for hw in hotword_list_or_file.strip().split():
|
|
hotword_str_list.append(hw)
|
|
hw_list = hw.strip().split()
|
|
if seg_dict is not None:
|
|
hw_list = seg_tokenize(hw_list, seg_dict)
|
|
hotword_list.append(tokenizer.tokens2ids(hw_list))
|
|
hotword_list.append([self.sos])
|
|
hotword_str_list.append('<s>')
|
|
logging.info("Hotword list: {}.".format(hotword_str_list))
|
|
else:
|
|
hotword_list = None
|
|
return hotword_list
|
|
|
|
|
|
class ParaformerOnline(Paraformer):
|
|
"""
|
|
Author: Speech Lab of DAMO Academy, Alibaba Group
|
|
Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
|
|
https://arxiv.org/abs/2206.08317
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
*args,
|
|
**kwargs,
|
|
):
|
|
|
|
super().__init__(*args, **kwargs)
|
|
|
|
# import pdb;
|
|
# pdb.set_trace()
|
|
self.sampling_ratio = kwargs.get("sampling_ratio", 0.2)
|
|
|
|
|
|
self.scama_mask = None
|
|
if hasattr(self.encoder, "overlap_chunk_cls") and self.encoder.overlap_chunk_cls is not None:
|
|
from funasr.modules.streaming_utils.chunk_utilis import build_scama_mask_for_cross_attention_decoder
|
|
self.build_scama_mask_for_cross_attention_decoder_fn = build_scama_mask_for_cross_attention_decoder
|
|
self.decoder_attention_chunk_type = kwargs.get("decoder_attention_chunk_type", "chunk")
|
|
|
|
|
|
|
|
def forward(
|
|
self,
|
|
speech: torch.Tensor,
|
|
speech_lengths: torch.Tensor,
|
|
text: torch.Tensor,
|
|
text_lengths: torch.Tensor,
|
|
**kwargs,
|
|
) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
|
|
"""Encoder + Decoder + Calc loss
|
|
Args:
|
|
speech: (Batch, Length, ...)
|
|
speech_lengths: (Batch, )
|
|
text: (Batch, Length)
|
|
text_lengths: (Batch,)
|
|
"""
|
|
# import pdb;
|
|
# pdb.set_trace()
|
|
decoding_ind = kwargs.get("decoding_ind")
|
|
if len(text_lengths.size()) > 1:
|
|
text_lengths = text_lengths[:, 0]
|
|
if len(speech_lengths.size()) > 1:
|
|
speech_lengths = speech_lengths[:, 0]
|
|
|
|
batch_size = speech.shape[0]
|
|
|
|
# Encoder
|
|
if hasattr(self.encoder, "overlap_chunk_cls"):
|
|
ind = self.encoder.overlap_chunk_cls.random_choice(self.training, decoding_ind)
|
|
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths, ind=ind)
|
|
else:
|
|
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
|
|
|
|
loss_ctc, cer_ctc = None, None
|
|
loss_pre = None
|
|
stats = dict()
|
|
|
|
# decoder: CTC branch
|
|
|
|
if self.ctc_weight > 0.0:
|
|
if hasattr(self.encoder, "overlap_chunk_cls"):
|
|
encoder_out_ctc, encoder_out_lens_ctc = self.encoder.overlap_chunk_cls.remove_chunk(encoder_out,
|
|
encoder_out_lens,
|
|
chunk_outs=None)
|
|
else:
|
|
encoder_out_ctc, encoder_out_lens_ctc = encoder_out, encoder_out_lens
|
|
|
|
loss_ctc, cer_ctc = self._calc_ctc_loss(
|
|
encoder_out_ctc, encoder_out_lens_ctc, text, text_lengths
|
|
)
|
|
# Collect CTC branch stats
|
|
stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None
|
|
stats["cer_ctc"] = cer_ctc
|
|
|
|
# decoder: Attention decoder branch
|
|
loss_att, acc_att, cer_att, wer_att, loss_pre, pre_loss_att = self._calc_att_predictor_loss(
|
|
encoder_out, encoder_out_lens, text, text_lengths
|
|
)
|
|
|
|
# 3. CTC-Att loss definition
|
|
if self.ctc_weight == 0.0:
|
|
loss = loss_att + loss_pre * self.predictor_weight
|
|
else:
|
|
loss = self.ctc_weight * loss_ctc + (
|
|
1 - self.ctc_weight) * loss_att + loss_pre * self.predictor_weight
|
|
|
|
# Collect Attn branch stats
|
|
stats["loss_att"] = loss_att.detach() if loss_att is not None else None
|
|
stats["pre_loss_att"] = pre_loss_att.detach() if pre_loss_att is not None else None
|
|
stats["acc"] = acc_att
|
|
stats["cer"] = cer_att
|
|
stats["wer"] = wer_att
|
|
stats["loss_pre"] = loss_pre.detach().cpu() if loss_pre is not None else None
|
|
|
|
stats["loss"] = torch.clone(loss.detach())
|
|
|
|
# force_gatherable: to-device and to-tensor if scalar for DataParallel
|
|
if self.length_normalized_loss:
|
|
batch_size = (text_lengths + self.predictor_bias).sum()
|
|
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
|
|
return loss, stats, weight
|
|
|
|
def encode_chunk(
|
|
self, speech: torch.Tensor, speech_lengths: torch.Tensor, cache: dict = None, **kwargs,
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
"""Frontend + Encoder. Note that this method is used by asr_inference.py
|
|
Args:
|
|
speech: (Batch, Length, ...)
|
|
speech_lengths: (Batch, )
|
|
ind: int
|
|
"""
|
|
with autocast(False):
|
|
|
|
# Data augmentation
|
|
if self.specaug is not None and self.training:
|
|
speech, speech_lengths = self.specaug(speech, speech_lengths)
|
|
|
|
# Normalization for feature: e.g. Global-CMVN, Utterance-CMVN
|
|
if self.normalize is not None:
|
|
speech, speech_lengths = self.normalize(speech, speech_lengths)
|
|
|
|
# Forward encoder
|
|
encoder_out, encoder_out_lens, _ = self.encoder.forward_chunk(speech, speech_lengths, cache=cache["encoder"])
|
|
if isinstance(encoder_out, tuple):
|
|
encoder_out = encoder_out[0]
|
|
|
|
return encoder_out, torch.tensor([encoder_out.size(1)])
|
|
|
|
def _calc_att_predictor_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
|
|
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_pad,
|
|
encoder_out_mask,
|
|
ignore_id=self.ignore_id,
|
|
mask_chunk_predictor=mask_chunk_predictor,
|
|
target_label_length=ys_pad_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_pad_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)
|
|
# 0. sampler
|
|
decoder_out_1st = None
|
|
pre_loss_att = None
|
|
if self.sampling_ratio > 0.0:
|
|
if self.step_cur < 2:
|
|
logging.info("enable sampler in paraformer, sampling_ratio: {}".format(self.sampling_ratio))
|
|
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, scama_mask)
|
|
else:
|
|
sematic_embeds, decoder_out_1st = \
|
|
self.sampler(encoder_out, encoder_out_lens, ys_pad,
|
|
ys_pad_lens, pre_acoustic_embeds, scama_mask)
|
|
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, scama_mask
|
|
)
|
|
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, chunk_mask=None):
|
|
|
|
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, chunk_mask
|
|
)
|
|
decoder_out, _ = decoder_outs[0], decoder_outs[1]
|
|
pred_tokens = decoder_out.argmax(-1)
|
|
nonpad_positions = ys_pad.ne(self.ignore_id)
|
|
seq_lens = (nonpad_positions).sum(1)
|
|
same_num = ((pred_tokens == ys_pad) & nonpad_positions).sum(1)
|
|
input_mask = torch.ones_like(nonpad_positions)
|
|
bsz, seq_len = ys_pad.size()
|
|
for li in range(bsz):
|
|
target_num = (((seq_lens[li] - same_num[li].sum()).float()) * self.sampling_ratio).long()
|
|
if target_num > 0:
|
|
input_mask[li].scatter_(dim=0, index=torch.randperm(seq_lens[li])[:target_num].cuda(), value=0)
|
|
input_mask = input_mask.eq(1)
|
|
input_mask = input_mask.masked_fill(~nonpad_positions, False)
|
|
input_mask_expand_dim = input_mask.unsqueeze(2).to(pre_acoustic_embeds.device)
|
|
|
|
sematic_embeds = pre_acoustic_embeds.masked_fill(~input_mask_expand_dim, 0) + ys_pad_embed.masked_fill(
|
|
input_mask_expand_dim, 0)
|
|
return sematic_embeds * tgt_mask, decoder_out * tgt_mask
|
|
|
|
|
|
def calc_predictor(self, encoder_out, encoder_out_lens):
|
|
|
|
encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
|
|
encoder_out.device)
|
|
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, pre_peak_index = self.predictor(encoder_out,
|
|
None,
|
|
encoder_out_mask,
|
|
ignore_id=self.ignore_id,
|
|
mask_chunk_predictor=mask_chunk_predictor,
|
|
target_label_length=None,
|
|
)
|
|
predictor_alignments, predictor_alignments_len = self.predictor.gen_frame_alignments(pre_alphas,
|
|
encoder_out_lens + 1 if self.predictor.tail_threshold > 0.0 else 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=None,
|
|
is_training=self.training,
|
|
)
|
|
self.scama_mask = scama_mask
|
|
|
|
return pre_acoustic_embeds, pre_token_length, pre_alphas, pre_peak_index
|
|
|
|
def calc_predictor_chunk(self, encoder_out, cache=None):
|
|
|
|
pre_acoustic_embeds, pre_token_length = \
|
|
self.predictor.forward_chunk(encoder_out, cache["encoder"])
|
|
return pre_acoustic_embeds, pre_token_length
|
|
|
|
def cal_decoder_with_predictor(self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens):
|
|
decoder_outs = self.decoder(
|
|
encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, self.scama_mask
|
|
)
|
|
decoder_out = decoder_outs[0]
|
|
decoder_out = torch.log_softmax(decoder_out, dim=-1)
|
|
return decoder_out, ys_pad_lens
|
|
|
|
def cal_decoder_with_predictor_chunk(self, encoder_out, sematic_embeds, cache=None):
|
|
decoder_outs = self.decoder.forward_chunk(
|
|
encoder_out, sematic_embeds, cache["decoder"]
|
|
)
|
|
decoder_out = decoder_outs
|
|
decoder_out = torch.log_softmax(decoder_out, dim=-1)
|
|
return decoder_out
|
|
|
|
def generate(self,
|
|
speech: torch.Tensor,
|
|
speech_lengths: torch.Tensor,
|
|
tokenizer=None,
|
|
**kwargs,
|
|
):
|
|
|
|
is_use_ctc = kwargs.get("ctc_weight", 0.0) > 0.00001 and self.ctc != None
|
|
print(is_use_ctc)
|
|
is_use_lm = kwargs.get("lm_weight", 0.0) > 0.00001 and kwargs.get("lm_file", None) is not None
|
|
|
|
if self.beam_search is None and (is_use_lm or is_use_ctc):
|
|
logging.info("enable beam_search")
|
|
self.init_beam_search(speech, speech_lengths, **kwargs)
|
|
self.nbest = kwargs.get("nbest", 1)
|
|
|
|
# Forward Encoder
|
|
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
|
|
if isinstance(encoder_out, tuple):
|
|
encoder_out = encoder_out[0]
|
|
|
|
# predictor
|
|
predictor_outs = self.calc_predictor(encoder_out, encoder_out_lens)
|
|
pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = predictor_outs[0], predictor_outs[1], \
|
|
predictor_outs[2], predictor_outs[3]
|
|
pre_token_length = pre_token_length.round().long()
|
|
if torch.max(pre_token_length) < 1:
|
|
return []
|
|
decoder_outs = self.cal_decoder_with_predictor(encoder_out, encoder_out_lens, pre_acoustic_embeds,
|
|
pre_token_length)
|
|
decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
|
|
|
|
results = []
|
|
b, n, d = decoder_out.size()
|
|
for i in range(b):
|
|
x = encoder_out[i, :encoder_out_lens[i], :]
|
|
am_scores = decoder_out[i, :pre_token_length[i], :]
|
|
if self.beam_search is not None:
|
|
nbest_hyps = self.beam_search(
|
|
x=x, am_scores=am_scores, maxlenratio=kwargs.get("maxlenratio", 0.0),
|
|
minlenratio=kwargs.get("minlenratio", 0.0)
|
|
)
|
|
|
|
nbest_hyps = nbest_hyps[: self.nbest]
|
|
else:
|
|
|
|
yseq = am_scores.argmax(dim=-1)
|
|
score = am_scores.max(dim=-1)[0]
|
|
score = torch.sum(score, dim=-1)
|
|
# pad with mask tokens to ensure compatibility with sos/eos tokens
|
|
yseq = torch.tensor(
|
|
[self.sos] + yseq.tolist() + [self.eos], device=yseq.device
|
|
)
|
|
nbest_hyps = [Hypothesis(yseq=yseq, score=score)]
|
|
for hyp in nbest_hyps:
|
|
assert isinstance(hyp, (Hypothesis)), type(hyp)
|
|
|
|
# remove sos/eos and get results
|
|
last_pos = -1
|
|
if isinstance(hyp.yseq, list):
|
|
token_int = hyp.yseq[1:last_pos]
|
|
else:
|
|
token_int = hyp.yseq[1:last_pos].tolist()
|
|
|
|
# remove blank symbol id, which is assumed to be 0
|
|
token_int = list(filter(lambda x: x != 0 and x != 2, token_int))
|
|
|
|
# Change integer-ids to tokens
|
|
token = tokenizer.ids2tokens(token_int)
|
|
text = tokenizer.tokens2text(token)
|
|
|
|
timestamp = []
|
|
|
|
results.append((text, token, timestamp))
|
|
|
|
return results
|
|
|