mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
605 lines
23 KiB
Python
605 lines
23 KiB
Python
#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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import time
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import copy
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import torch
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import logging
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from torch.cuda.amp import autocast
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from typing import Union, Dict, List, Tuple, Optional
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from funasr.register import tables
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from funasr.models.ctc.ctc import CTC
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from funasr.utils import postprocess_utils
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from funasr.metrics.compute_acc import th_accuracy
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from funasr.train_utils.device_funcs import to_device
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from funasr.utils.datadir_writer import DatadirWriter
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from funasr.models.paraformer.search import Hypothesis
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from funasr.models.paraformer.cif_predictor import mae_loss
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from funasr.train_utils.device_funcs import force_gatherable
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from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
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from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
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from funasr.models.transformer.utils.nets_utils import make_pad_mask
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from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard
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from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
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@tables.register("model_classes", "Paraformer")
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class Paraformer(torch.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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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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if specaug is not None:
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specaug_class = tables.specaug_classes.get(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 = tables.normalize_classes.get(normalize)
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normalize = normalize_class(**normalize_conf)
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encoder_class = tables.encoder_classes.get(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 = tables.decoder_classes.get(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(odim=vocab_size, encoder_output_size=encoder_output_size, **ctc_conf)
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if predictor is not None:
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predictor_class = tables.predictor_classes.get(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.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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self.error_calculator = 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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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 = (
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self.ctc_weight * loss_ctc
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+ (1 - self.ctc_weight) * loss_att
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+ loss_pre * self.predictor_weight
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)
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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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stats["batch_size"] = batch_size
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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,
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speech: torch.Tensor,
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speech_lengths: torch.Tensor,
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**kwargs,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""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 = (
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~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]
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).to(encoder_out.device)
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pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = self.predictor(
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encoder_out, None, encoder_out_mask, ignore_id=self.ignore_id
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)
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return pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index
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def cal_decoder_with_predictor(
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self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens
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):
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decoder_outs = self.decoder(encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens)
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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 = (
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~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]
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).to(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(
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encoder_out, ys_pad, encoder_out_mask, ignore_id=self.ignore_id
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)
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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(
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encoder_out, encoder_out_lens, ys_pad, ys_pad_lens, pre_acoustic_embeds
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)
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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(encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens)
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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(
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ys_pad.device
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)
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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 = (
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((seq_lens[li] - same_num[li].sum()).float()) * self.sampling_ratio
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).long()
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if target_num > 0:
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input_mask[li].scatter_(
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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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)
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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(
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~input_mask_expand_dim, 0
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) + ys_pad_embed.masked_fill(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(
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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.models.transformer.scorers.ctc import CTCPrefixScorer
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from funasr.models.transformer.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(ctc=ctc)
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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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# 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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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,
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scorers=scorers,
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sos=self.sos,
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eos=self.eos,
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vocab_size=len(token_list),
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token_list=token_list,
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pre_beam_score_key=None if self.ctc_weight == 1.0 else "full",
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)
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# beam_search.to(device=kwargs.get("device", "cpu"), dtype=getattr(torch, kwargs.get("dtype", "float32"))).eval()
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# for scorer in scorers.values():
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# if isinstance(scorer, torch.nn.Module):
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# scorer.to(device=kwargs.get("device", "cpu"), dtype=getattr(torch, kwargs.get("dtype", "float32"))).eval()
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self.beam_search = beam_search
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def inference(
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self,
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data_in,
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data_lengths=None,
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key: list = None,
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tokenizer=None,
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frontend=None,
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**kwargs,
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):
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# init beamsearch
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is_use_ctc = kwargs.get("decoding_ctc_weight", 0.0) > 0.00001 and self.ctc != None
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is_use_lm = (
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kwargs.get("lm_weight", 0.0) > 0.00001 and kwargs.get("lm_file", None) is not None
|
|
)
|
|
pred_timestamp = kwargs.get("pred_timestamp", False)
|
|
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 = {}
|
|
if (
|
|
isinstance(data_in, torch.Tensor) and kwargs.get("data_type", "sound") == "fbank"
|
|
): # fbank
|
|
speech, speech_lengths = data_in, data_lengths
|
|
if len(speech.shape) < 3:
|
|
speech = speech[None, :, :]
|
|
if speech_lengths is not None:
|
|
speech_lengths = speech_lengths.squeeze(-1)
|
|
else:
|
|
speech_lengths = speech.shape[1]
|
|
else:
|
|
# extract fbank feats
|
|
time1 = time.perf_counter()
|
|
audio_sample_list = load_audio_text_image_video(
|
|
data_in,
|
|
fs=frontend.fs,
|
|
audio_fs=kwargs.get("fs", 16000),
|
|
data_type=kwargs.get("data_type", "sound"),
|
|
tokenizer=tokenizer,
|
|
)
|
|
time2 = time.perf_counter()
|
|
meta_data["load_data"] = f"{time2 - time1:0.3f}"
|
|
speech, speech_lengths = extract_fbank(
|
|
audio_sample_list, data_type=kwargs.get("data_type", "sound"), frontend=frontend
|
|
)
|
|
time3 = time.perf_counter()
|
|
meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
|
|
meta_data["batch_data_time"] = (
|
|
speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
|
|
)
|
|
|
|
speech = speech.to(device=kwargs["device"])
|
|
speech_lengths = speech_lengths.to(device=kwargs["device"])
|
|
# Encoder
|
|
if kwargs.get("fp16", False):
|
|
speech = speech.half()
|
|
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()
|
|
if isinstance(key[0], (list, tuple)):
|
|
key = key[0]
|
|
if len(key) < b:
|
|
key = key * b
|
|
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 kwargs.get("output_dir") is not None:
|
|
if not hasattr(self, "writer"):
|
|
self.writer = DatadirWriter(kwargs.get("output_dir"))
|
|
ibest_writer = self.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
|
|
)
|
|
)
|
|
|
|
if tokenizer is not None:
|
|
# Change integer-ids to tokens
|
|
token = tokenizer.ids2tokens(token_int)
|
|
text_postprocessed = tokenizer.tokens2text(token)
|
|
|
|
if pred_timestamp:
|
|
timestamp_str, timestamp = ts_prediction_lfr6_standard(
|
|
pre_peak_index[i],
|
|
alphas[i],
|
|
copy.copy(token),
|
|
vad_offset=kwargs.get("begin_time", 0),
|
|
upsample_rate=1,
|
|
)
|
|
if not hasattr(tokenizer, "bpemodel"):
|
|
text_postprocessed, time_stamp_postprocessed, _ = postprocess_utils.sentence_postprocess(token, timestamp)
|
|
result_i = {"key": key[i], "text": text_postprocessed, "timestamp": time_stamp_postprocessed,}
|
|
else:
|
|
if not hasattr(tokenizer, "bpemodel"):
|
|
text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
|
|
result_i = {"key": key[i], "text": text_postprocessed}
|
|
|
|
if ibest_writer is not None:
|
|
ibest_writer["token"][key[i]] = " ".join(token)
|
|
# ibest_writer["text"][key[i]] = text
|
|
ibest_writer["text"][key[i]] = text_postprocessed
|
|
else:
|
|
result_i = {"key": key[i], "token_int": token_int}
|
|
results.append(result_i)
|
|
|
|
return results, meta_data
|
|
|
|
def export(self, **kwargs):
|
|
from .export_meta import export_rebuild_model
|
|
|
|
if "max_seq_len" not in kwargs:
|
|
kwargs["max_seq_len"] = 512
|
|
models = export_rebuild_model(model=self, **kwargs)
|
|
return models
|