import logging
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from typing import Union, Dict, List, Tuple, Optional
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|
import time
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import torch
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import numpy as np
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import torch.nn as nn
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from torch.cuda.amp import autocast
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from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
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from funasr.models.ctc.ctc import CTC
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from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
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from funasr.metrics.compute_acc import th_accuracy
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from funasr.train_utils.device_funcs import force_gatherable
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from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
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from funasr.utils import postprocess_utils
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from funasr.utils.datadir_writer import DatadirWriter
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from funasr.register import tables
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@tables.register("model_classes", "OpenAIWhisperModel")
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class OpenAIWhisperModel(nn.Module):
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"""CTC-attention hybrid Encoder-Decoder model"""
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def __init__(
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self,
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specaug: str = None,
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specaug_conf: dict = None,
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normalize: str = None,
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normalize_conf: dict = None,
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encoder: str = None,
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encoder_conf: dict = None,
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decoder: str = None,
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decoder_conf: dict = None,
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ctc: str = None,
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ctc_conf: dict = None,
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ctc_weight: float = 0.5,
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interctc_weight: float = 0.0,
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input_size: int = 80,
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vocab_size: int = -1,
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ignore_id: int = -1,
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blank_id: int = 0,
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sos: int = 1,
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eos: int = 2,
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lsm_weight: float = 0.0,
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length_normalized_loss: bool = False,
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report_cer: bool = True,
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report_wer: bool = True,
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sym_space: str = "<space>",
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sym_blank: str = "<blank>",
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# extract_feats_in_collect_stats: bool = True,
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share_embedding: bool = False,
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# preencoder: Optional[AbsPreEncoder] = None,
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# postencoder: Optional[AbsPostEncoder] = None,
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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(decoder_conf)
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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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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.specaug = specaug
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self.normalize = normalize
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self.encoder = encoder
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if not hasattr(self.encoder, "interctc_use_conditioning"):
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self.encoder.interctc_use_conditioning = False
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if self.encoder.interctc_use_conditioning:
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self.encoder.conditioning_layer = torch.nn.Linear(
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vocab_size, self.encoder.output_size()
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)
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self.interctc_weight = interctc_weight
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# self.error_calculator = None
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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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self.error_calculator = None
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if ctc_weight == 0.0:
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self.ctc = None
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else:
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self.ctc = ctc
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self.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.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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# 1. Encoder
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encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
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intermediate_outs = None
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if isinstance(encoder_out, tuple):
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intermediate_outs = encoder_out[1]
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encoder_out = encoder_out[0]
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loss_att, acc_att, cer_att, wer_att = None, None, None, None
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loss_ctc, cer_ctc = None, None
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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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# Intermediate CTC (optional)
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loss_interctc = 0.0
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if self.interctc_weight != 0.0 and intermediate_outs is not None:
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for layer_idx, intermediate_out in intermediate_outs:
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# we assume intermediate_out has the same length & padding
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# as those of encoder_out
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loss_ic, cer_ic = self._calc_ctc_loss(
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intermediate_out, encoder_out_lens, text, text_lengths
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)
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loss_interctc = loss_interctc + loss_ic
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# Collect Intermedaite CTC stats
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stats["loss_interctc_layer{}".format(layer_idx)] = (
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loss_ic.detach() if loss_ic is not None else None
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)
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stats["cer_interctc_layer{}".format(layer_idx)] = cer_ic
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loss_interctc = loss_interctc / len(intermediate_outs)
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# calculate whole encoder loss
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loss_ctc = (
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1 - self.interctc_weight
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) * loss_ctc + self.interctc_weight * loss_interctc
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# decoder: Attention decoder branch
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loss_att, acc_att, cer_att, wer_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
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elif self.ctc_weight == 1.0:
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loss = loss_ctc
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else:
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loss = self.ctc_weight * loss_ctc + (1 - self.ctc_weight) * loss_att
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# Collect Attn branch stats
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stats["loss_att"] = loss_att.detach() if loss_att is not None else None
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stats["acc"] = acc_att
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stats["cer"] = cer_att
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stats["wer"] = wer_att
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# Collect total loss stats
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stats["loss"] = torch.clone(loss.detach())
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# force_gatherable: to-device and to-tensor if scalar for DataParallel
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if self.length_normalized_loss:
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batch_size = int((text_lengths + 1).sum())
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loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
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return loss, stats, weight
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def 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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# feats: (Batch, Length, Dim)
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# -> encoder_out: (Batch, Length2, Dim2)
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if self.encoder.interctc_use_conditioning:
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encoder_out, encoder_out_lens, _ = self.encoder(
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speech, speech_lengths, ctc=self.ctc
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)
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else:
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encoder_out, encoder_out_lens, _ = self.encoder(speech, speech_lengths)
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intermediate_outs = None
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if isinstance(encoder_out, tuple):
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intermediate_outs = encoder_out[1]
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encoder_out = encoder_out[0]
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if intermediate_outs is not None:
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return (encoder_out, intermediate_outs), encoder_out_lens
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return encoder_out, encoder_out_lens
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def _calc_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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ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
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ys_in_lens = ys_pad_lens + 1
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# 1. Forward decoder
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decoder_out, _ = self.decoder(
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encoder_out, encoder_out_lens, ys_in_pad, ys_in_lens
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)
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# 2. Compute attention loss
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loss_att = self.criterion_att(decoder_out, ys_out_pad)
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acc_att = th_accuracy(
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decoder_out.view(-1, self.vocab_size),
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ys_out_pad,
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ignore_label=self.ignore_id,
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)
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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.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
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def _calc_ctc_loss(
|
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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# 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,
|
):
|
from funasr.models.transformer.search import BeamSearch
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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(
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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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decoder=self.decoder,
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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", 0.5),
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ctc=kwargs.get("decoding_ctc_weight", 0.5),
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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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beam_search = BeamSearch(
|
beam_size=kwargs.get("beam_size", 10),
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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,
|
pre_beam_score_key=None if self.ctc_weight == 1.0 else "full",
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)
|
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self.beam_search = beam_search
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def inference(self,
|
data_in,
|
data_lengths=None,
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key: list=None,
|
tokenizer=None,
|
frontend=None,
|
**kwargs,
|
):
|
|
if kwargs.get("batch_size", 1) > 1:
|
raise NotImplementedError("batch decoding is not implemented")
|
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# init beamsearch
|
if self.beam_search is None:
|
logging.info("enable beam_search")
|
self.init_beam_search(**kwargs)
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self.nbest = kwargs.get("nbest", 1)
|
|
meta_data = {}
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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 None:
|
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"),
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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"),
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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
|
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
|
if isinstance(encoder_out, tuple):
|
encoder_out = encoder_out[0]
|
|
# c. Passed the encoder result and the beam search
|
nbest_hyps = self.beam_search(
|
x=encoder_out[0], maxlenratio=kwargs.get("maxlenratio", 0.0), minlenratio=kwargs.get("minlenratio", 0.0)
|
)
|
|
nbest_hyps = nbest_hyps[: self.nbest]
|
|
|
results = []
|
b, n, d = encoder_out.size()
|
for i in range(b):
|
|
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))
|
|
# 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_postprocessed}
|
results.append(result_i)
|
|
if ibest_writer is not None:
|
ibest_writer["token"][key[i]] = " ".join(token)
|
ibest_writer["text"][key[i]] = text_postprocessed
|
|
return results, meta_data
|
|
|
@tables.register("model_classes", "OpenAIWhisperLIDModel")
|
class OpenAIWhisperLIDModel(nn.Module):
|
"""WhisperEncoder and EResNet based LID Model"""
|
|
def __init__(
|
self,
|
vocab_size: int,
|
specaug: str = None,
|
specaug_conf: dict = None,
|
encoder: str = None,
|
encoder_conf: dict = None,
|
lid_predictor: str = None,
|
lid_predictor_conf: dict = None,
|
proj_dim: int = None,
|
clip_frames: int = None,
|
random_clip: bool = False,
|
**kwargs,
|
):
|
super().__init__()
|
if specaug is not None:
|
specaug_class = tables.specaug_classes.get(specaug)
|
specaug = specaug_class(**specaug_conf)
|
encoder_class = tables.encoder_classes.get(encoder)
|
encoder = encoder_class(**encoder_conf)
|
lid_predictor_class = tables.lid_predictor_classes.get(lid_predictor)
|
lid_predictor = lid_predictor_class(**lid_predictor_conf)
|
if encoder.output_size() != proj_dim:
|
self.proj_layer = torch.nn.Linear(encoder.output_size(), proj_dim)
|
else:
|
self.proj_layer = None
|
self.output_layer = torch.nn.Linear(lid_predictor.output_size(), vocab_size)
|
self.criterion_lid = LabelSmoothingLoss(
|
size=vocab_size,
|
padding_idx=-1,
|
smoothing=0.0,
|
normalize_length=False,
|
)
|
|
self.specaug = specaug
|
self.encoder = encoder
|
self.lid_predictor = lid_predictor
|
self.clip_frames = clip_frames
|
self.random_clip = random_clip
|
self.normalize = None
|
self.beam_search = None
|
if not hasattr(self.encoder, "interctc_use_conditioning"):
|
self.encoder.interctc_use_conditioning = False
|
|
def forward(self,
|
speech: torch.Tensor, # may be padding
|
speech_lengths: torch.Tensor, # actual length
|
lid: torch.Tensor, # lid label, (batch_size, 1)
|
lid_lengths: torch.Tensor,
|
):
|
assert lid.shape[1] == 1
|
batch_size = speech.shape[0]
|
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
|
|
# re-generate encoder_out
|
if self.clip_frames is None:
|
reduced_encoder_out = torch.zeros(batch_size, encoder_out_lens.max(), encoder_out.shape[-1]).to(encoder_out.dtype).to(encoder_out.device)
|
for i, enc_length in enumerate(encoder_out_lens):
|
reduced_encoder_out[i, :enc_length] = encoder_out[i, :enc_length]
|
else:
|
reduced_encoder_out = torch.zeros(batch_size, self.clip_frames, encoder_out.shape[-1]).to(encoder_out.dtype).to(encoder_out.device)
|
if self.random_clip:
|
for i, enc_length in enumerate(encoder_out_lens):
|
if enc_length <= self.clip_frames:
|
reduced_encoder_out[i, :enc_length] = encoder_out[i, :enc_length]
|
encoder_out_lens[i] = enc_length
|
else:
|
max_start_index = enc_length.item() - self.clip_frames
|
start_index = np.random.randint(0, max_start_index + 1)
|
reduced_encoder_out[i, :self.clip_frames] = encoder_out[i, start_index:start_index + self.clip_frames]
|
encoder_out_lens[i] = self.clip_frames
|
else:
|
for i, enc_length in enumerate(encoder_out_lens):
|
enc_length = self.clip_frames if enc_length >= self.clip_frames else enc_length
|
reduced_encoder_out[i, :enc_length] = encoder_out[i, :enc_length]
|
encoder_out_lens[i] = enc_length
|
if self.proj_layer is not None:
|
reduced_encoder_out = self.proj_layer(reduced_encoder_out)
|
lid_output = self.lid_predictor(reduced_encoder_out, encoder_out_lens) # (B, D)
|
lid_logits = self.output_layer(lid_output) # (B, num_classes)
|
loss = self.criterion_lid(lid_logits[:, None, :], lid)
|
with torch.no_grad():
|
_, predicted_lid = torch.max(lid_logits, 1)
|
correct = (predicted_lid == lid[:, 0]).sum().item()
|
lid_acc = correct * 1.0 / lid_logits.shape[0]
|
stats = dict()
|
stats["batch_size"] = batch_size
|
stats["loss"] = torch.clone(loss.detach())
|
stats["acc"] = lid_acc
|
stats["token_length"] = speech_lengths.max()
|
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
|
return loss, stats, weight
|
|
def encode(
|
self, speech: torch.Tensor, speech_lengths: torch.Tensor
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
"""Frontend + Encoder. Note that this method is used by asr_inference.py
|
Args:
|
speech: (Batch, Length, ...)
|
speech_lengths: (Batch, )
|
"""
|
with autocast(False):
|
|
# Data augmentation
|
if self.specaug is not None and self.training:
|
speech = speech.permute(0, 2, 1)
|
# suit for whisper padding
|
padded_speech_lengths = torch.ones_like(speech_lengths) * speech.shape[1]
|
speech, padded_speech_lengths = self.specaug(speech, padded_speech_lengths)
|
speech = speech.permute(0, 2, 1)
|
|
# 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
|
# feats: (Batch, Length, Dim)
|
# -> encoder_out: (Batch, Length2, Dim2)
|
if self.encoder.interctc_use_conditioning:
|
encoder_out, encoder_out_lens, _ = self.encoder(
|
speech, speech_lengths, ctc=self.ctc
|
)
|
else:
|
encoder_out, encoder_out_lens, _ = self.encoder(speech, speech_lengths)
|
intermediate_outs = None
|
if isinstance(encoder_out, tuple):
|
intermediate_outs = encoder_out[1]
|
encoder_out = encoder_out[0]
|
|
if intermediate_outs is not None:
|
return (encoder_out, intermediate_outs), encoder_out_lens
|
|
return encoder_out, encoder_out_lens
|
|
def inference(self,
|
data_in,
|
data_lengths=None,
|
key: list = None,
|
tokenizer=None,
|
frontend=None,
|
**kwargs,
|
):
|
|
if kwargs.get("batch_size", 1) > 1:
|
raise NotImplementedError("batch decoding is not implemented")
|
|
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 None:
|
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
|
enc, enc_out_lens = self.encode(speech, speech_lengths)
|
|
inference_clip_length = kwargs.get("inference_clip_length", None)
|
if self.clip_frames is not None:
|
if inference_clip_length is None:
|
reduced_enc = torch.zeros(enc.shape[0], self.clip_frames, enc.shape[-1]).to(enc.dtype).to(enc.device)
|
for i, enc_length in enumerate(enc_out_lens):
|
enc_length = self.clip_frames if enc_length >= self.clip_frames else enc_length
|
reduced_enc[i, :enc_length] = enc[i, :enc_length]
|
enc_out_lens[i] = enc_length
|
else:
|
assert inference_clip_length > 0, "inference_clip_length must be larger than 0"
|
reduced_enc = torch.zeros(enc.shape[0], inference_clip_length, enc.shape[-1]).to(enc.dtype).to(enc.device)
|
for i, enc_length in enumerate(enc_out_lens):
|
enc_length = inference_clip_length if enc_length >= inference_clip_length else enc_length
|
reduced_enc[i, :enc_length] = enc[i, :enc_length]
|
enc_out_lens[i] = enc_length
|
else:
|
reduced_enc = torch.zeros(enc.shape[0], enc_out_lens.max(), enc.shape[-1]).to(enc.dtype).to(enc.device)
|
for i, enc_length in enumerate(enc_out_lens):
|
reduced_enc[i, :enc_length] = enc[i, :enc_length]
|
|
if self.proj_layer is not None:
|
reduced_enc = self.proj_layer(reduced_enc)
|
lid_output = self.lid_predictor(reduced_enc, enc_out_lens) # (B, D)
|
lid_logits = self.output_layer(lid_output) # (B, num_classes)
|
|
_, predicted_lid_index = torch.max(lid_logits, 1)
|
predicted_lid = tokenizer.ids2tokens([predicted_lid_index[0].cpu()])[0]
|
|
if kwargs.get("output_dir") is not None:
|
if not hasattr(self, "writer"):
|
self.writer = DatadirWriter(kwargs.get("output_dir"))
|
lid_writer = self.writer["lid"]
|
lid_writer[key[0]] = predicted_lid
|
|
results = [{"key": key[0], "lid": predicted_lid}]
|
|
return results, meta_data
|
