Rename 'res' in line 514 to avoid with naming conflict with line 365

liugz18

2024-07-18 d80ac2fd2df4e7fb8a28acfa512bb11472b5cc99

 funasr/models/contextual_paraformer/model.py

@@ -1,42 +1,31 @@
#!/usr/bin/env python3
# -*- encoding: utf-8 -*-
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
#  MIT License  (https://opensource.org/licenses/MIT)

import os
import re
import time
import torch
import codecs
import logging
import tempfile
import requests
import numpy as np
from typing import Dict, Tuple
from contextlib import contextmanager
from distutils.version import LooseVersion
from typing import Dict
from typing import List
from typing import Optional
from typing import Tuple
from typing import Union
import tempfile
import codecs
import requests
import re
import copy
import torch
import torch.nn as nn
import random
import numpy as np
import time
# from funasr.layers.abs_normalize import AbsNormalize
from funasr.losses.label_smoothing_loss import (
    LabelSmoothingLoss,  # noqa: H301
)
# from funasr.models.ctc import CTC
# from funasr.models.decoder.abs_decoder import AbsDecoder
# from funasr.models.e2e_asr_common import ErrorCalculator
# from funasr.models.encoder.abs_encoder import AbsEncoder
# from funasr.frontends.abs_frontend import AbsFrontend
# from funasr.models.postencoder.abs_postencoder import AbsPostEncoder
from funasr.models.paraformer.cif_predictor import mae_loss
# from funasr.models.preencoder.abs_preencoder import AbsPreEncoder
# from funasr.models.specaug.abs_specaug import AbsSpecAug

from funasr.register import tables
from funasr.utils import postprocess_utils
from funasr.metrics.compute_acc import th_accuracy
from funasr.models.paraformer.model import Paraformer
from funasr.utils.datadir_writer import DatadirWriter
from funasr.models.paraformer.search import Hypothesis
from funasr.train_utils.device_funcs import force_gatherable
from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
from funasr.models.transformer.utils.nets_utils import make_pad_mask, pad_list
from funasr.metrics.compute_acc import th_accuracy
from funasr.train_utils.device_funcs import force_gatherable
# from funasr.models.base_model import FunASRModel
# from funasr.models.paraformer.cif_predictor import CifPredictorV3
from funasr.models.paraformer.search import Hypothesis
from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank


if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
@@ -46,14 +35,7 @@
    @contextmanager
    def autocast(enabled=True):
        yield
from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
from funasr.utils import postprocess_utils
from funasr.utils.datadir_writer import DatadirWriter


from funasr.models.paraformer.model import Paraformer

from funasr.register import tables

@tables.register("model_classes", "ContextualParaformer")
class ContextualParaformer(Paraformer):
@@ -62,14 +44,14 @@
    FunASR: A Fundamental End-to-End Speech Recognition Toolkit
    https://arxiv.org/abs/2305.11013
    """
    

    def __init__(
        self,
        *args,
        **kwargs,
    ):
        super().__init__(*args, **kwargs)
        

        self.target_buffer_length = kwargs.get("target_buffer_length", -1)
        inner_dim = kwargs.get("inner_dim", 256)
        bias_encoder_type = kwargs.get("bias_encoder_type", "lstm")
@@ -78,17 +60,16 @@
        crit_attn_smooth = kwargs.get("crit_attn_smooth", 0.0)
        bias_encoder_dropout_rate = kwargs.get("bias_encoder_dropout_rate", 0.0)


        if bias_encoder_type == 'lstm':
            logging.warning("enable bias encoder sampling and contextual training")
            self.bias_encoder = torch.nn.LSTM(inner_dim, inner_dim, 1, batch_first=True, dropout=bias_encoder_dropout_rate)
        if bias_encoder_type == "lstm":
            self.bias_encoder = torch.nn.LSTM(
                inner_dim, inner_dim, 1, batch_first=True, dropout=bias_encoder_dropout_rate
            )
            self.bias_embed = torch.nn.Embedding(self.vocab_size, inner_dim)
        elif bias_encoder_type == 'mean':
            logging.warning("enable bias encoder sampling and contextual training")
        elif bias_encoder_type == "mean":
            self.bias_embed = torch.nn.Embedding(self.vocab_size, inner_dim)
        else:
            logging.error("Unsupport bias encoder type: {}".format(bias_encoder_type))
        

        if self.target_buffer_length > 0:
            self.hotword_buffer = None
            self.length_record = []
@@ -99,7 +80,6 @@
            self.attn_loss = torch.nn.L1Loss()
        self.crit_attn_smooth = crit_attn_smooth


    def forward(
        self,
        speech: torch.Tensor,
@@ -109,74 +89,73 @@
        **kwargs,
    ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
        """Frontend + Encoder + Decoder + Calc loss
    

        Args:
                speech: (Batch, Length, ...)
                speech_lengths: (Batch, )
                text: (Batch, Length)
                text_lengths: (Batch,)
        """
        if len(text_lengths.size()) > 1:
            text_lengths = text_lengths[:, 0]
        if len(speech_lengths.size()) > 1:
            speech_lengths = speech_lengths[:, 0]
        
        text_lengths = text_lengths.squeeze()
        speech_lengths = speech_lengths.squeeze()

        batch_size = speech.shape[0]

        hotword_pad = kwargs.get("hotword_pad")
        hotword_lengths = kwargs.get("hotword_lengths")
        dha_pad = kwargs.get("dha_pad")
        
        # dha_pad = kwargs.get("dha_pad")

        # 1. Encoder
        encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)

        
        loss_ctc, cer_ctc = None, None
        

        stats = dict()
        

        # 1. CTC branch
        if self.ctc_weight != 0.0:
            loss_ctc, cer_ctc = self._calc_ctc_loss(
                encoder_out, encoder_out_lens, text, text_lengths
            )
            

            # Collect CTC branch stats
            stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None
            stats["cer_ctc"] = cer_ctc
        

        # 2b. Attention decoder branch
        loss_att, acc_att, cer_att, wer_att, loss_pre, loss_ideal = self._calc_att_clas_loss(
            encoder_out, encoder_out_lens, text, text_lengths, hotword_pad, hotword_lengths
        )
        

        # 3. CTC-Att loss definition
        if self.ctc_weight == 0.0:
            loss = loss_att + loss_pre * self.predictor_weight
        else:
            loss = self.ctc_weight * loss_ctc + (1 - self.ctc_weight) * loss_att + loss_pre * self.predictor_weight
        
            loss = (
                self.ctc_weight * loss_ctc
                + (1 - self.ctc_weight) * loss_att
                + loss_pre * self.predictor_weight
            )

        if loss_ideal is not None:
            loss = loss + loss_ideal * self.crit_attn_weight
            stats["loss_ideal"] = loss_ideal.detach().cpu()
        

        # Collect Attn branch stats
        stats["loss_att"] = loss_att.detach() if loss_att is not None else None
        stats["acc"] = acc_att
        stats["cer"] = cer_att
        stats["wer"] = wer_att
        stats["loss_pre"] = loss_pre.detach().cpu() if loss_pre is not None else None
        

        stats["loss"] = torch.clone(loss.detach())
        # force_gatherable: to-device and to-tensor if scalar for DataParallel
        if self.length_normalized_loss:
            batch_size = int((text_lengths + self.predictor_bias).sum())
        

        loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
        return loss, stats, weight
    
    

    def _calc_att_clas_loss(
        self,
        encoder_out: torch.Tensor,
@@ -186,42 +165,53 @@
        hotword_pad: torch.Tensor,
        hotword_lengths: torch.Tensor,
    ):
        encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
            encoder_out.device)
        encoder_out_mask = (
            ~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]
        ).to(encoder_out.device)

        if self.predictor_bias == 1:
            _, ys_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
            ys_pad_lens = ys_pad_lens + self.predictor_bias
        pre_acoustic_embeds, pre_token_length, _, _ = self.predictor(encoder_out, ys_pad, encoder_out_mask,
                                                                     ignore_id=self.ignore_id)
        

        pre_acoustic_embeds, pre_token_length, _, _ = self.predictor(
            encoder_out, ys_pad, encoder_out_mask, ignore_id=self.ignore_id
        )
        # -1. bias encoder
        if self.use_decoder_embedding:
            hw_embed = self.decoder.embed(hotword_pad)
        else:
            hw_embed = self.bias_embed(hotword_pad)

        hw_embed, (_, _) = self.bias_encoder(hw_embed)
        _ind = np.arange(0, hotword_pad.shape[0]).tolist()
        selected = hw_embed[_ind, [i - 1 for i in hotword_lengths.detach().cpu().tolist()]]
        contextual_info = selected.squeeze(0).repeat(ys_pad.shape[0], 1, 1).to(ys_pad.device)
        

        # 0. sampler
        decoder_out_1st = None
        if self.sampling_ratio > 0.0:
            if self.step_cur < 2:
                logging.info("enable sampler in paraformer, sampling_ratio: {}".format(self.sampling_ratio))
            sematic_embeds, decoder_out_1st = self.sampler(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens,
                                                           pre_acoustic_embeds, contextual_info)

            sematic_embeds, decoder_out_1st = self.sampler(
                encoder_out,
                encoder_out_lens,
                ys_pad,
                ys_pad_lens,
                pre_acoustic_embeds,
                contextual_info,
            )
        else:
            if self.step_cur < 2:
                logging.info("disable sampler in paraformer, sampling_ratio: {}".format(self.sampling_ratio))
            sematic_embeds = pre_acoustic_embeds
        

        # 1. Forward decoder
        decoder_outs = self.decoder(
            encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, contextual_info=contextual_info
            encoder_out,
            encoder_out_lens,
            sematic_embeds,
            ys_pad_lens,
            contextual_info=contextual_info,
        )
        decoder_out, _ = decoder_outs[0], decoder_outs[1]
        '''
        """
        if self.crit_attn_weight > 0 and attn.shape[-1] > 1:
            ideal_attn = ideal_attn + self.crit_attn_smooth / (self.crit_attn_smooth + 1.0)
            attn_non_blank = attn[:,:,:,:-1]
@@ -229,9 +219,9 @@
            loss_ideal = self.attn_loss(attn_non_blank.max(1)[0], ideal_attn_non_blank.to(attn.device))
        else:
            loss_ideal = None
        '''
        """
        loss_ideal = None
        

        if decoder_out_1st is None:
            decoder_out_1st = decoder_out
        # 2. Compute attention loss
@@ -242,19 +232,28 @@
            ignore_label=self.ignore_id,
        )
        loss_pre = self.criterion_pre(ys_pad_lens.type_as(pre_token_length), pre_token_length)
        

        # Compute cer/wer using attention-decoder
        if self.training or self.error_calculator is None:
            cer_att, wer_att = None, None
        else:
            ys_hat = decoder_out_1st.argmax(dim=-1)
            cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu())
        

        return loss_att, acc_att, cer_att, wer_att, loss_pre, loss_ideal
    
    
    def sampler(self, encoder_out, encoder_out_lens, ys_pad, ys_pad_lens, pre_acoustic_embeds, contextual_info):
        tgt_mask = (~make_pad_mask(ys_pad_lens, maxlen=ys_pad_lens.max())[:, :, None]).to(ys_pad.device)

    def sampler(
        self,
        encoder_out,
        encoder_out_lens,
        ys_pad,
        ys_pad_lens,
        pre_acoustic_embeds,
        contextual_info,
    ):
        tgt_mask = (~make_pad_mask(ys_pad_lens, maxlen=ys_pad_lens.max())[:, :, None]).to(
            ys_pad.device
        )
        ys_pad = ys_pad * tgt_mask[:, :, 0]
        if self.share_embedding:
            ys_pad_embed = self.decoder.output_layer.weight[ys_pad]
@@ -262,7 +261,11 @@
            ys_pad_embed = self.decoder.embed(ys_pad)
        with torch.no_grad():
            decoder_outs = self.decoder(
                encoder_out, encoder_out_lens, pre_acoustic_embeds, ys_pad_lens, contextual_info=contextual_info
                encoder_out,
                encoder_out_lens,
                pre_acoustic_embeds,
                ys_pad_lens,
                contextual_info=contextual_info,
            )
            decoder_out, _ = decoder_outs[0], decoder_outs[1]
            pred_tokens = decoder_out.argmax(-1)
@@ -272,22 +275,35 @@
            input_mask = torch.ones_like(nonpad_positions)
            bsz, seq_len = ys_pad.size()
            for li in range(bsz):
                target_num = (((seq_lens[li] - same_num[li].sum()).float()) * self.sampling_ratio).long()
                target_num = (
                    ((seq_lens[li] - same_num[li].sum()).float()) * self.sampling_ratio
                ).long()
                if target_num > 0:
                    input_mask[li].scatter_(dim=0,
                                            index=torch.randperm(seq_lens[li])[:target_num].to(pre_acoustic_embeds.device),
                                            value=0)
                    input_mask[li].scatter_(
                        dim=0,
                        index=torch.randperm(seq_lens[li])[:target_num].to(
                            pre_acoustic_embeds.device
                        ),
                        value=0,
                    )
            input_mask = input_mask.eq(1)
            input_mask = input_mask.masked_fill(~nonpad_positions, False)
            input_mask_expand_dim = input_mask.unsqueeze(2).to(pre_acoustic_embeds.device)
        
        sematic_embeds = pre_acoustic_embeds.masked_fill(~input_mask_expand_dim, 0) + ys_pad_embed.masked_fill(
            input_mask_expand_dim, 0)

        sematic_embeds = pre_acoustic_embeds.masked_fill(
            ~input_mask_expand_dim, 0
        ) + ys_pad_embed.masked_fill(input_mask_expand_dim, 0)
        return sematic_embeds * tgt_mask, decoder_out * tgt_mask
    
    
    def cal_decoder_with_predictor(self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, hw_list=None,
                                   clas_scale=1.0):

    def cal_decoder_with_predictor(
        self,
        encoder_out,
        encoder_out_lens,
        sematic_embeds,
        ys_pad_lens,
        hw_list=None,
        clas_scale=1.0,
    ):
        if hw_list is None:
            hw_list = [torch.Tensor([1]).long().to(encoder_out.device)]  # empty hotword list
            hw_list_pad = pad_list(hw_list, 0)
@@ -299,127 +315,159 @@
            hw_embed = h_n.repeat(encoder_out.shape[0], 1, 1)
        else:
            hw_lengths = [len(i) for i in hw_list]
            hw_list_pad = pad_list([torch.Tensor(i).long() for i in hw_list], 0).to(encoder_out.device)
            hw_list_pad = pad_list([torch.Tensor(i).long() for i in hw_list], 0).to(
                encoder_out.device
            )
            if self.use_decoder_embedding:
                hw_embed = self.decoder.embed(hw_list_pad)
            else:
                hw_embed = self.bias_embed(hw_list_pad)
            hw_embed = torch.nn.utils.rnn.pack_padded_sequence(hw_embed, hw_lengths, batch_first=True,
                                                               enforce_sorted=False)
            hw_embed = torch.nn.utils.rnn.pack_padded_sequence(
                hw_embed, hw_lengths, batch_first=True, enforce_sorted=False
            )
            _, (h_n, _) = self.bias_encoder(hw_embed)
            hw_embed = h_n.repeat(encoder_out.shape[0], 1, 1)
        

        decoder_outs = self.decoder(
            encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, contextual_info=hw_embed, clas_scale=clas_scale
            encoder_out,
            encoder_out_lens,
            sematic_embeds,
            ys_pad_lens,
            contextual_info=hw_embed,
            clas_scale=clas_scale,
        )

        decoder_out = decoder_outs[0]
        decoder_out = torch.log_softmax(decoder_out, dim=-1)
        return decoder_out, ys_pad_lens
        
    def generate(self,
                 data_in,
                 data_lengths=None,
                 key: list = None,
                 tokenizer=None,
                 frontend=None,
                 **kwargs,
                 ):
        

    def inference(
        self,
        data_in,
        data_lengths=None,
        key: list = None,
        tokenizer=None,
        frontend=None,
        **kwargs,
    ):
        # init beamsearch

        is_use_ctc = kwargs.get("decoding_ctc_weight", 0.0) > 0.00001 and self.ctc != None
        is_use_lm = kwargs.get("lm_weight", 0.0) > 0.00001 and kwargs.get("lm_file", None) is not None
        is_use_lm = (
            kwargs.get("lm_weight", 0.0) > 0.00001 and kwargs.get("lm_file", None) is not None
        )
        if self.beam_search is None and (is_use_lm or is_use_ctc):
            logging.info("enable beam_search")
            self.init_beam_search(**kwargs)
            self.nbest = kwargs.get("nbest", 1)
        

        meta_data = {}
        

        # extract fbank feats
        time1 = time.perf_counter()
        audio_sample_list = load_audio_text_image_video(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000))

        audio_sample_list = load_audio_text_image_video(
            data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000)
        )

        time2 = time.perf_counter()
        meta_data["load_data"] = f"{time2 - time1:0.3f}"
        speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
                                               frontend=frontend)

        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
        
        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"])

        # hotword
        self.hotword_list = self.generate_hotwords_list(kwargs.get("hotword", None), tokenizer=tokenizer, frontend=frontend)
        
        self.hotword_list = self.generate_hotwords_list(
            kwargs.get("hotword", None), tokenizer=tokenizer, frontend=frontend
        )

        # Encoder
        encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
        if isinstance(encoder_out, tuple):
            encoder_out = encoder_out[0]
        

        # predictor
        predictor_outs = self.calc_predictor(encoder_out, encoder_out_lens)
        pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = predictor_outs[0], predictor_outs[1], \
                                                                        predictor_outs[2], predictor_outs[3]
        pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = (
            predictor_outs[0],
            predictor_outs[1],
            predictor_outs[2],
            predictor_outs[3],
        )
        pre_token_length = pre_token_length.round().long()
        if torch.max(pre_token_length) < 1:
            return []


        decoder_outs = self.cal_decoder_with_predictor(encoder_out, encoder_out_lens,
                                                                 pre_acoustic_embeds,
                                                                 pre_token_length,
                                                                 hw_list=self.hotword_list,
                                                                 clas_scale=kwargs.get("clas_scale", 1.0))
        decoder_outs = self.cal_decoder_with_predictor(
            encoder_out,
            encoder_out_lens,
            pre_acoustic_embeds,
            pre_token_length,
            hw_list=self.hotword_list,
            clas_scale=kwargs.get("clas_scale", 1.0),
        )
        decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
        

        results = []
        b, n, d = decoder_out.size()
        for i in range(b):
            x = encoder_out[i, :encoder_out_lens[i], :]
            am_scores = decoder_out[i, :pre_token_length[i], :]
            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)
                    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
                )
                yseq = torch.tensor([self.sos] + yseq.tolist() + [self.eos], device=yseq.device)
                nbest_hyps = [Hypothesis(yseq=yseq, score=score)]
            for nbest_idx, hyp in enumerate(nbest_hyps):
                ibest_writer = None
                if ibest_writer is None and kwargs.get("output_dir") is not None:
                    writer = DatadirWriter(kwargs.get("output_dir"))
                    ibest_writer = writer[f"{nbest_idx + 1}best_recog"]
                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))
                
                    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 = tokenizer.tokens2text(token)
                    

                    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
@@ -427,9 +475,8 @@
                else:
                    result_i = {"key": key[i], "token_int": token_int}
                results.append(result_i)
        
        return results, meta_data

        return results, meta_data

    def generate_hotwords_list(self, hotword_list_or_file, tokenizer=None, frontend=None):
        def load_seg_dict(seg_dict_file):
@@ -443,9 +490,9 @@
                    value = s[1:]
                    seg_dict[key] = " ".join(value)
            return seg_dict
        

        def seg_tokenize(txt, seg_dict):
            pattern = re.compile(r'^[\u4E00-\u9FA50-9]+$')
            pattern = re.compile(r"^[\u4E00-\u9FA50-9]+$")
            out_txt = ""
            for word in txt:
                word = word.lower()
@@ -461,11 +508,11 @@
                    else:
                        out_txt += "<unk>" + " "
            return out_txt.strip().split()
        

        seg_dict = None
        if frontend.cmvn_file is not None:
            model_dir = os.path.dirname(frontend.cmvn_file)
            seg_dict_file = os.path.join(model_dir, 'seg_dict')
            seg_dict_file = os.path.join(model_dir, "seg_dict")
            if os.path.exists(seg_dict_file):
                seg_dict = load_seg_dict(seg_dict_file)
            else:
@@ -474,11 +521,11 @@
        if hotword_list_or_file is None:
            hotword_list = None
        # for local txt inputs
        elif os.path.exists(hotword_list_or_file) and hotword_list_or_file.endswith('.txt'):
        elif os.path.exists(hotword_list_or_file) and hotword_list_or_file.endswith(".txt"):
            logging.info("Attempting to parse hotwords from local txt...")
            hotword_list = []
            hotword_str_list = []
            with codecs.open(hotword_list_or_file, 'r') as fin:
            with codecs.open(hotword_list_or_file, "r") as fin:
                for line in fin.readlines():
                    hw = line.strip()
                    hw_list = hw.split()
@@ -487,11 +534,14 @@
                    hotword_str_list.append(hw)
                    hotword_list.append(tokenizer.tokens2ids(hw_list))
                hotword_list.append([self.sos])
                hotword_str_list.append('<s>')
            logging.info("Initialized hotword list from file: {}, hotword list: {}."
                         .format(hotword_list_or_file, hotword_str_list))
                hotword_str_list.append("<s>")
            logging.info(
                "Initialized hotword list from file: {}, hotword list: {}.".format(
                    hotword_list_or_file, hotword_str_list
                )
            )
        # for url, download and generate txt
        elif hotword_list_or_file.startswith('http'):
        elif hotword_list_or_file.startswith("http"):
            logging.info("Attempting to parse hotwords from url...")
            work_dir = tempfile.TemporaryDirectory().name
            if not os.path.exists(work_dir):
@@ -502,7 +552,7 @@
            hotword_list_or_file = text_file_path
            hotword_list = []
            hotword_str_list = []
            with codecs.open(hotword_list_or_file, 'r') as fin:
            with codecs.open(hotword_list_or_file, "r") as fin:
                for line in fin.readlines():
                    hw = line.strip()
                    hw_list = hw.split()
@@ -511,11 +561,14 @@
                    hotword_str_list.append(hw)
                    hotword_list.append(tokenizer.tokens2ids(hw_list))
                hotword_list.append([self.sos])
                hotword_str_list.append('<s>')
            logging.info("Initialized hotword list from file: {}, hotword list: {}."
                         .format(hotword_list_or_file, hotword_str_list))
                hotword_str_list.append("<s>")
            logging.info(
                "Initialized hotword list from file: {}, hotword list: {}.".format(
                    hotword_list_or_file, hotword_str_list
                )
            )
        # for text str input
        elif not hotword_list_or_file.endswith('.txt'):
        elif not hotword_list_or_file.endswith(".txt"):
            logging.info("Attempting to parse hotwords as str...")
            hotword_list = []
            hotword_str_list = []
@@ -526,9 +579,19 @@
                    hw_list = seg_tokenize(hw_list, seg_dict)
                hotword_list.append(tokenizer.tokens2ids(hw_list))
            hotword_list.append([self.sos])
            hotword_str_list.append('<s>')
            hotword_str_list.append("<s>")
            logging.info("Hotword list: {}.".format(hotword_str_list))
        else:
            hotword_list = None
        return hotword_list

    def export(
        self,
        **kwargs,
    ):
        if "max_seq_len" not in kwargs:
            kwargs["max_seq_len"] = 512
        from .export_meta import export_rebuild_model

        models = export_rebuild_model(model=self, **kwargs)
        return models