#!/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 copy
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import time
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import torch
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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, List, Optional, Tuple
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from funasr.register import tables
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from funasr.models.ctc.ctc import CTC
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from funasr.utils import postprocess_utils
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from funasr.metrics.compute_acc import th_accuracy
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from funasr.utils.datadir_writer import DatadirWriter
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from funasr.models.paraformer.model import Paraformer
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from funasr.models.paraformer.search import Hypothesis
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from funasr.train_utils.device_funcs import force_gatherable
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from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
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from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard
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from funasr.models.transformer.utils.nets_utils import make_pad_mask, pad_list
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from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
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from funasr.train_utils.device_funcs import to_device
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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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@tables.register("model_classes", "BiCifParaformer")
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class BiCifParaformer(Paraformer):
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"""
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Author: Speech Lab of DAMO Academy, Alibaba Group
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Paper1: FunASR: A Fundamental End-to-End Speech Recognition Toolkit
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https://arxiv.org/abs/2305.11013
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Paper2: Achieving timestamp prediction while recognizing with non-autoregressive end-to-end ASR model
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https://arxiv.org/abs/2301.12343
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"""
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def __init__(
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self,
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*args,
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**kwargs,
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):
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super().__init__(*args, **kwargs)
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def _calc_pre2_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_token_length2 = self.predictor(encoder_out, ys_pad, encoder_out_mask, ignore_id=self.ignore_id)
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# loss_pre = self.criterion_pre(ys_pad_lens.type_as(pre_token_length), pre_token_length)
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loss_pre2 = self.criterion_pre(ys_pad_lens.type_as(pre_token_length2), pre_token_length2)
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return loss_pre2
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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,
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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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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
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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, pre_token_length2 = self.predictor(encoder_out,
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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 calc_predictor_timestamp(self, encoder_out, encoder_out_lens, token_num):
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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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ds_alphas, ds_cif_peak, us_alphas, us_peaks = self.predictor.get_upsample_timestamp(encoder_out,
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encoder_out_mask,
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token_num)
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return ds_alphas, ds_cif_peak, us_alphas, us_peaks
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def forward(
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self,
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speech: torch.Tensor,
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speech_lengths: torch.Tensor,
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text: torch.Tensor,
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text_lengths: torch.Tensor,
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**kwargs,
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) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
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"""Frontend + Encoder + Decoder + Calc loss
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Args:
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speech: (Batch, Length, ...)
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speech_lengths: (Batch, )
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text: (Batch, Length)
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text_lengths: (Batch,)
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"""
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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 = self._calc_att_loss(
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encoder_out, encoder_out_lens, text, text_lengths
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)
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loss_pre2 = self._calc_pre2_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 + loss_pre2 * self.predictor_weight * 0.5
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else:
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loss = self.ctc_weight * loss_ctc + (
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1 - self.ctc_weight) * loss_att + loss_pre * self.predictor_weight + loss_pre2 * self.predictor_weight * 0.5
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# Collect Attn branch stats
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stats["loss_att"] = loss_att.detach() if loss_att is not None else None
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stats["acc"] = acc_att
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stats["cer"] = cer_att
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stats["wer"] = wer_att
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stats["loss_pre"] = loss_pre.detach().cpu() if loss_pre is not None else None
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stats["loss_pre2"] = loss_pre2.detach().cpu()
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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 + 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 inference(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 = kwargs.get("lm_weight", 0.0) > 0.00001 and kwargs.get("lm_file", None) is not None
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if self.beam_search is None and (is_use_lm or is_use_ctc):
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logging.info("enable beam_search")
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self.init_beam_search(**kwargs)
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self.nbest = kwargs.get("nbest", 1)
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meta_data = {}
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# if isinstance(data_in, torch.Tensor): # fbank
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# speech, speech_lengths = data_in, data_lengths
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# if len(speech.shape) < 3:
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# speech = speech[None, :, :]
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# if speech_lengths is None:
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# speech_lengths = speech.shape[1]
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# else:
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# extract fbank feats
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time1 = time.perf_counter()
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audio_sample_list = load_audio_text_image_video(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000))
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time2 = time.perf_counter()
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meta_data["load_data"] = f"{time2 - time1:0.3f}"
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speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
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frontend=frontend)
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time3 = time.perf_counter()
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meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
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meta_data["batch_data_time"] = speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
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speech = speech.to(device=kwargs["device"])
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speech_lengths = speech_lengths.to(device=kwargs["device"])
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# Encoder
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encoder_out, encoder_out_lens = self.encode(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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# predictor
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predictor_outs = self.calc_predictor(encoder_out, encoder_out_lens)
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pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = predictor_outs[0], predictor_outs[1], \
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predictor_outs[2], predictor_outs[3]
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pre_token_length = pre_token_length.round().long()
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if torch.max(pre_token_length) < 1:
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return []
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decoder_outs = self.cal_decoder_with_predictor(encoder_out, encoder_out_lens, pre_acoustic_embeds,
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pre_token_length)
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decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
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# BiCifParaformer, test no bias cif2
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_, _, us_alphas, us_peaks = self.calc_predictor_timestamp(encoder_out, encoder_out_lens,
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pre_token_length)
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results = []
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b, n, d = decoder_out.size()
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for i in range(b):
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x = encoder_out[i, :encoder_out_lens[i], :]
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am_scores = decoder_out[i, :pre_token_length[i], :]
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if self.beam_search is not None:
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nbest_hyps = self.beam_search(
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x=x, am_scores=am_scores, maxlenratio=kwargs.get("maxlenratio", 0.0),
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minlenratio=kwargs.get("minlenratio", 0.0)
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)
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nbest_hyps = nbest_hyps[: self.nbest]
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else:
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yseq = am_scores.argmax(dim=-1)
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score = am_scores.max(dim=-1)[0]
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score = torch.sum(score, dim=-1)
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# pad with mask tokens to ensure compatibility with sos/eos tokens
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yseq = torch.tensor(
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[self.sos] + yseq.tolist() + [self.eos], device=yseq.device
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)
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nbest_hyps = [Hypothesis(yseq=yseq, score=score)]
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for nbest_idx, hyp in enumerate(nbest_hyps):
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ibest_writer = None
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if kwargs.get("output_dir") is not None:
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if not hasattr(self, "writer"):
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self.writer = DatadirWriter(kwargs.get("output_dir"))
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ibest_writer = self.writer[f"{nbest_idx+1}best_recog"]
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# remove sos/eos and get results
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last_pos = -1
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if isinstance(hyp.yseq, list):
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token_int = hyp.yseq[1:last_pos]
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else:
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token_int = hyp.yseq[1:last_pos].tolist()
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# remove blank symbol id, which is assumed to be 0
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token_int = list(filter(lambda x: x != self.eos and x != self.sos and x != self.blank_id, token_int))
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if tokenizer is not None:
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# Change integer-ids to tokens
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token = tokenizer.ids2tokens(token_int)
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text = tokenizer.tokens2text(token)
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_, timestamp = ts_prediction_lfr6_standard(us_alphas[i][:encoder_out_lens[i] * 3],
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us_peaks[i][:encoder_out_lens[i] * 3],
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copy.copy(token),
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vad_offset=kwargs.get("begin_time", 0))
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text_postprocessed, time_stamp_postprocessed, word_lists = postprocess_utils.sentence_postprocess(
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token, timestamp)
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result_i = {"key": key[i], "text": text_postprocessed,
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"timestamp": time_stamp_postprocessed,
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}
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if ibest_writer is not None:
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ibest_writer["token"][key[i]] = " ".join(token)
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# ibest_writer["text"][key[i]] = text
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ibest_writer["timestamp"][key[i]] = time_stamp_postprocessed
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ibest_writer["text"][key[i]] = text_postprocessed
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else:
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result_i = {"key": key[i], "token_int": token_int}
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results.append(result_i)
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return results, meta_data
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def export(
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self,
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max_seq_len=512,
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**kwargs,
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):
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self.device = kwargs.get("device")
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is_onnx = kwargs.get("type", "onnx") == "onnx"
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encoder_class = tables.encoder_classes.get(kwargs["encoder"] + "Export")
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self.encoder = encoder_class(self.encoder, onnx=is_onnx)
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predictor_class = tables.predictor_classes.get(kwargs["predictor"] + "Export")
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self.predictor = predictor_class(self.predictor, onnx=is_onnx)
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decoder_class = tables.decoder_classes.get(kwargs["decoder"] + "Export")
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self.decoder = decoder_class(self.decoder, onnx=is_onnx)
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from funasr.utils.torch_function import MakePadMask
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from funasr.utils.torch_function import sequence_mask
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if is_onnx:
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self.make_pad_mask = MakePadMask(max_seq_len, flip=False)
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else:
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self.make_pad_mask = sequence_mask(max_seq_len, flip=False)
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self.forward = self._export_forward
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return self
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def export_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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):
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# a. To device
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batch = {"speech": speech, "speech_lengths": speech_lengths}
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batch = to_device(batch, device=self.device)
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enc, enc_len = self.encoder(**batch)
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mask = self.make_pad_mask(enc_len)[:, None, :]
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pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = self.predictor(enc, mask)
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pre_token_length = pre_token_length.round().type(torch.int32)
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decoder_out, _ = self.decoder(enc, enc_len, pre_acoustic_embeds, pre_token_length)
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decoder_out = torch.log_softmax(decoder_out, dim=-1)
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# get predicted timestamps
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us_alphas, us_cif_peak = self.predictor.get_upsample_timestmap(enc, mask, pre_token_length)
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return decoder_out, pre_token_length, us_alphas, us_cif_peak
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def export_dummy_inputs(self):
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speech = torch.randn(2, 30, 560)
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speech_lengths = torch.tensor([6, 30], dtype=torch.int32)
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return (speech, speech_lengths)
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def export_input_names(self):
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return ['speech', 'speech_lengths']
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def export_output_names(self):
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return ['logits', 'token_num', 'us_alphas', 'us_cif_peak']
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def export_dynamic_axes(self):
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return {
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'speech': {
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0: 'batch_size',
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1: 'feats_length'
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},
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'speech_lengths': {
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0: 'batch_size',
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},
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'logits': {
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0: 'batch_size',
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1: 'logits_length'
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},
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'us_alphas': {
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0: 'batch_size',
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1: 'alphas_length'
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},
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'us_cif_peak': {
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0: 'batch_size',
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1: 'alphas_length'
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},
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}
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def export_name(self, ):
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return "model.onnx"
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