FunASR/funasr/models/paraformer_streaming/model.py

import os
import logging
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.paraformer.cif_predictor import mae_loss

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.paraformer.search import Hypothesis

if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
	from torch.cuda.amp import autocast
else:
	# Nothing to do if torch<1.6.0
	@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.utils.timestamp_tools import ts_prediction_lfr6_standard

from funasr.models.ctc.ctc import CTC
from funasr.models.paraformer.model import Paraformer

from funasr.register import tables

@tables.register("model_classes", "ParaformerStreaming")
class ParaformerStreaming(Paraformer):
	"""
	Author: Speech Lab of DAMO Academy, Alibaba Group
	Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
	https://arxiv.org/abs/2206.08317
	"""
	
	def __init__(
		self,
		*args,
		**kwargs,
	):
		
		super().__init__(*args, **kwargs)
		
		# import pdb;
		# pdb.set_trace()
		self.sampling_ratio = kwargs.get("sampling_ratio", 0.2)


		self.scama_mask = None
		if hasattr(self.encoder, "overlap_chunk_cls") and self.encoder.overlap_chunk_cls is not None:
			from funasr.models.scama.chunk_utilis import build_scama_mask_for_cross_attention_decoder
			self.build_scama_mask_for_cross_attention_decoder_fn = build_scama_mask_for_cross_attention_decoder
			self.decoder_attention_chunk_type = kwargs.get("decoder_attention_chunk_type", "chunk")


	
	def forward(
		self,
		speech: torch.Tensor,
		speech_lengths: torch.Tensor,
		text: torch.Tensor,
		text_lengths: torch.Tensor,
		**kwargs,
	) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
		"""Encoder + Decoder + Calc loss
		Args:
				speech: (Batch, Length, ...)
				speech_lengths: (Batch, )
				text: (Batch, Length)
				text_lengths: (Batch,)
		"""
		# import pdb;
		# pdb.set_trace()
		decoding_ind = kwargs.get("decoding_ind")
		if len(text_lengths.size()) > 1:
			text_lengths = text_lengths[:, 0]
		if len(speech_lengths.size()) > 1:
			speech_lengths = speech_lengths[:, 0]
		
		batch_size = speech.shape[0]
		
		# Encoder
		if hasattr(self.encoder, "overlap_chunk_cls"):
			ind = self.encoder.overlap_chunk_cls.random_choice(self.training, decoding_ind)
			encoder_out, encoder_out_lens = self.encode(speech, speech_lengths, ind=ind)
		else:
			encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
		
		loss_ctc, cer_ctc = None, None
		loss_pre = None
		stats = dict()
		
		# decoder: CTC branch

		if self.ctc_weight > 0.0:
			if hasattr(self.encoder, "overlap_chunk_cls"):
				encoder_out_ctc, encoder_out_lens_ctc = self.encoder.overlap_chunk_cls.remove_chunk(encoder_out,
				                                                                                    encoder_out_lens,
				                                                                                    chunk_outs=None)
			else:
				encoder_out_ctc, encoder_out_lens_ctc = encoder_out, encoder_out_lens
				
			loss_ctc, cer_ctc = self._calc_ctc_loss(
				encoder_out_ctc, encoder_out_lens_ctc, text, text_lengths
			)
			# Collect CTC branch stats
			stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None
			stats["cer_ctc"] = cer_ctc
		
		# decoder: Attention decoder branch
		loss_att, acc_att, cer_att, wer_att, loss_pre, pre_loss_att = self._calc_att_predictor_loss(
			encoder_out, encoder_out_lens, text, text_lengths
		)
		
		# 3. CTC-Att loss definition
		if self.ctc_weight == 0.0:
			loss = loss_att + loss_pre * self.predictor_weight
		else:
			loss = self.ctc_weight * loss_ctc + (
					1 - self.ctc_weight) * loss_att + loss_pre * self.predictor_weight
		
		# Collect Attn branch stats
		stats["loss_att"] = loss_att.detach() if loss_att is not None else None
		stats["pre_loss_att"] = pre_loss_att.detach() if pre_loss_att is not None else None
		stats["acc"] = acc_att
		stats["cer"] = cer_att
		stats["wer"] = wer_att
		stats["loss_pre"] = loss_pre.detach().cpu() if loss_pre is not None else None
		
		stats["loss"] = torch.clone(loss.detach())
		
		# force_gatherable: to-device and to-tensor if scalar for DataParallel
		if self.length_normalized_loss:
			batch_size = (text_lengths + self.predictor_bias).sum()
		loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
		return loss, stats, weight
	
	def encode_chunk(
		self, speech: torch.Tensor, speech_lengths: torch.Tensor, cache: dict = None, **kwargs,
	) -> Tuple[torch.Tensor, torch.Tensor]:
		"""Frontend + Encoder. Note that this method is used by asr_inference.py
		Args:
				speech: (Batch, Length, ...)
				speech_lengths: (Batch, )
				ind: int
		"""
		with autocast(False):
			
			# Data augmentation
			if self.specaug is not None and self.training:
				speech, speech_lengths = self.specaug(speech, speech_lengths)
			
			# Normalization for feature: e.g. Global-CMVN, Utterance-CMVN
			if self.normalize is not None:
				speech, speech_lengths = self.normalize(speech, speech_lengths)
		
		# Forward encoder
		encoder_out, encoder_out_lens, _ = self.encoder.forward_chunk(speech, speech_lengths, cache=cache["encoder"])
		if isinstance(encoder_out, tuple):
			encoder_out = encoder_out[0]
		
		return encoder_out, torch.tensor([encoder_out.size(1)])
	
	def _calc_att_predictor_loss(
		self,
		encoder_out: torch.Tensor,
		encoder_out_lens: torch.Tensor,
		ys_pad: torch.Tensor,
		ys_pad_lens: torch.Tensor,
	):
		encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
			encoder_out.device)
		if self.predictor_bias == 1:
			_, ys_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
			ys_pad_lens = ys_pad_lens + self.predictor_bias
		mask_chunk_predictor = None
		if self.encoder.overlap_chunk_cls is not None:
			mask_chunk_predictor = self.encoder.overlap_chunk_cls.get_mask_chunk_predictor(None,
			                                                                               device=encoder_out.device,
			                                                                               batch_size=encoder_out.size(
				                                                                               0))
			mask_shfit_chunk = self.encoder.overlap_chunk_cls.get_mask_shfit_chunk(None, device=encoder_out.device,
			                                                                       batch_size=encoder_out.size(0))
			encoder_out = encoder_out * mask_shfit_chunk
		pre_acoustic_embeds, pre_token_length, pre_alphas, _ = self.predictor(encoder_out,
		                                                                      ys_pad,
		                                                                      encoder_out_mask,
		                                                                      ignore_id=self.ignore_id,
		                                                                      mask_chunk_predictor=mask_chunk_predictor,
		                                                                      target_label_length=ys_pad_lens,
		                                                                      )
		predictor_alignments, predictor_alignments_len = self.predictor.gen_frame_alignments(pre_alphas,
		                                                                                     encoder_out_lens)
		
		scama_mask = None
		if self.encoder.overlap_chunk_cls is not None and self.decoder_attention_chunk_type == 'chunk':
			encoder_chunk_size = self.encoder.overlap_chunk_cls.chunk_size_pad_shift_cur
			attention_chunk_center_bias = 0
			attention_chunk_size = encoder_chunk_size
			decoder_att_look_back_factor = self.encoder.overlap_chunk_cls.decoder_att_look_back_factor_cur
			mask_shift_att_chunk_decoder = self.encoder.overlap_chunk_cls. \
				get_mask_shift_att_chunk_decoder(None,
			                                     device=encoder_out.device,
			                                     batch_size=encoder_out.size(0)
			                                     )
			scama_mask = self.build_scama_mask_for_cross_attention_decoder_fn(
				predictor_alignments=predictor_alignments,
				encoder_sequence_length=encoder_out_lens,
				chunk_size=1,
				encoder_chunk_size=encoder_chunk_size,
				attention_chunk_center_bias=attention_chunk_center_bias,
				attention_chunk_size=attention_chunk_size,
				attention_chunk_type=self.decoder_attention_chunk_type,
				step=None,
				predictor_mask_chunk_hopping=mask_chunk_predictor,
				decoder_att_look_back_factor=decoder_att_look_back_factor,
				mask_shift_att_chunk_decoder=mask_shift_att_chunk_decoder,
				target_length=ys_pad_lens,
				is_training=self.training,
			)
		elif self.encoder.overlap_chunk_cls is not None:
			encoder_out, encoder_out_lens = self.encoder.overlap_chunk_cls.remove_chunk(encoder_out,
			                                                                            encoder_out_lens,
			                                                                            chunk_outs=None)
		# 0. sampler
		decoder_out_1st = None
		pre_loss_att = None
		if self.sampling_ratio > 0.0:
			if self.step_cur < 2:
				logging.info("enable sampler in paraformer, sampling_ratio: {}".format(self.sampling_ratio))
			if self.use_1st_decoder_loss:
				sematic_embeds, decoder_out_1st, pre_loss_att = \
					self.sampler_with_grad(encoder_out, encoder_out_lens, ys_pad,
					                       ys_pad_lens, pre_acoustic_embeds, scama_mask)
			else:
				sematic_embeds, decoder_out_1st = \
					self.sampler(encoder_out, encoder_out_lens, ys_pad,
					             ys_pad_lens, pre_acoustic_embeds, scama_mask)
		else:
			if self.step_cur < 2:
				logging.info("disable sampler in paraformer, sampling_ratio: {}".format(self.sampling_ratio))
			sematic_embeds = pre_acoustic_embeds
		
		# 1. Forward decoder
		decoder_outs = self.decoder(
			encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, scama_mask
		)
		decoder_out, _ = decoder_outs[0], decoder_outs[1]
		
		if decoder_out_1st is None:
			decoder_out_1st = decoder_out
		# 2. Compute attention loss
		loss_att = self.criterion_att(decoder_out, ys_pad)
		acc_att = th_accuracy(
			decoder_out_1st.view(-1, self.vocab_size),
			ys_pad,
			ignore_label=self.ignore_id,
		)
		loss_pre = self.criterion_pre(ys_pad_lens.type_as(pre_token_length), pre_token_length)
		
		# Compute cer/wer using attention-decoder
		if self.training or self.error_calculator is None:
			cer_att, wer_att = None, None
		else:
			ys_hat = decoder_out_1st.argmax(dim=-1)
			cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu())
		
		return loss_att, acc_att, cer_att, wer_att, loss_pre, pre_loss_att
	
	def sampler(self, encoder_out, encoder_out_lens, ys_pad, ys_pad_lens, pre_acoustic_embeds, chunk_mask=None):
		
		tgt_mask = (~make_pad_mask(ys_pad_lens, maxlen=ys_pad_lens.max())[:, :, None]).to(ys_pad.device)
		ys_pad_masked = ys_pad * tgt_mask[:, :, 0]
		if self.share_embedding:
			ys_pad_embed = self.decoder.output_layer.weight[ys_pad_masked]
		else:
			ys_pad_embed = self.decoder.embed(ys_pad_masked)
		with torch.no_grad():
			decoder_outs = self.decoder(
				encoder_out, encoder_out_lens, pre_acoustic_embeds, ys_pad_lens, chunk_mask
			)
			decoder_out, _ = decoder_outs[0], decoder_outs[1]
			pred_tokens = decoder_out.argmax(-1)
			nonpad_positions = ys_pad.ne(self.ignore_id)
			seq_lens = (nonpad_positions).sum(1)
			same_num = ((pred_tokens == ys_pad) & nonpad_positions).sum(1)
			input_mask = torch.ones_like(nonpad_positions)
			bsz, seq_len = ys_pad.size()
			for li in range(bsz):
				target_num = (((seq_lens[li] - same_num[li].sum()).float()) * self.sampling_ratio).long()
				if target_num > 0:
					input_mask[li].scatter_(dim=0, index=torch.randperm(seq_lens[li])[:target_num].cuda(), value=0)
			input_mask = input_mask.eq(1)
			input_mask = input_mask.masked_fill(~nonpad_positions, False)
			input_mask_expand_dim = input_mask.unsqueeze(2).to(pre_acoustic_embeds.device)
		
		sematic_embeds = pre_acoustic_embeds.masked_fill(~input_mask_expand_dim, 0) + ys_pad_embed.masked_fill(
			input_mask_expand_dim, 0)
		return sematic_embeds * tgt_mask, decoder_out * tgt_mask
	

	def calc_predictor(self, encoder_out, encoder_out_lens):
		
		encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
			encoder_out.device)
		mask_chunk_predictor = None
		if self.encoder.overlap_chunk_cls is not None:
			mask_chunk_predictor = self.encoder.overlap_chunk_cls.get_mask_chunk_predictor(None,
			                                                                               device=encoder_out.device,
			                                                                               batch_size=encoder_out.size(
				                                                                               0))
			mask_shfit_chunk = self.encoder.overlap_chunk_cls.get_mask_shfit_chunk(None, device=encoder_out.device,
			                                                                       batch_size=encoder_out.size(0))
			encoder_out = encoder_out * mask_shfit_chunk
		pre_acoustic_embeds, pre_token_length, pre_alphas, pre_peak_index = self.predictor(encoder_out,
		                                                                                   None,
		                                                                                   encoder_out_mask,
		                                                                                   ignore_id=self.ignore_id,
		                                                                                   mask_chunk_predictor=mask_chunk_predictor,
		                                                                                   target_label_length=None,
		                                                                                   )
		predictor_alignments, predictor_alignments_len = self.predictor.gen_frame_alignments(pre_alphas,
		                                                                                     encoder_out_lens + 1 if self.predictor.tail_threshold > 0.0 else encoder_out_lens)
		
		scama_mask = None
		if self.encoder.overlap_chunk_cls is not None and self.decoder_attention_chunk_type == 'chunk':
			encoder_chunk_size = self.encoder.overlap_chunk_cls.chunk_size_pad_shift_cur
			attention_chunk_center_bias = 0
			attention_chunk_size = encoder_chunk_size
			decoder_att_look_back_factor = self.encoder.overlap_chunk_cls.decoder_att_look_back_factor_cur
			mask_shift_att_chunk_decoder = self.encoder.overlap_chunk_cls. \
				get_mask_shift_att_chunk_decoder(None,
			                                     device=encoder_out.device,
			                                     batch_size=encoder_out.size(0)
			                                     )
			scama_mask = self.build_scama_mask_for_cross_attention_decoder_fn(
				predictor_alignments=predictor_alignments,
				encoder_sequence_length=encoder_out_lens,
				chunk_size=1,
				encoder_chunk_size=encoder_chunk_size,
				attention_chunk_center_bias=attention_chunk_center_bias,
				attention_chunk_size=attention_chunk_size,
				attention_chunk_type=self.decoder_attention_chunk_type,
				step=None,
				predictor_mask_chunk_hopping=mask_chunk_predictor,
				decoder_att_look_back_factor=decoder_att_look_back_factor,
				mask_shift_att_chunk_decoder=mask_shift_att_chunk_decoder,
				target_length=None,
				is_training=self.training,
			)
		self.scama_mask = scama_mask
		
		return pre_acoustic_embeds, pre_token_length, pre_alphas, pre_peak_index
	
	def calc_predictor_chunk(self, encoder_out, encoder_out_lens, cache=None, **kwargs):
		is_final = kwargs.get("is_final", False)

		return self.predictor.forward_chunk(encoder_out, cache["encoder"], is_final=is_final)
	
	def cal_decoder_with_predictor(self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens):
		decoder_outs = self.decoder(
			encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, self.scama_mask
		)
		decoder_out = decoder_outs[0]
		decoder_out = torch.log_softmax(decoder_out, dim=-1)
		return decoder_out, ys_pad_lens
	
	def cal_decoder_with_predictor_chunk(self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, cache=None):
		decoder_outs = self.decoder.forward_chunk(
			encoder_out, sematic_embeds, cache["decoder"]
		)
		decoder_out = decoder_outs
		decoder_out = torch.log_softmax(decoder_out, dim=-1)
		return decoder_out, ys_pad_lens
	
	def init_cache(self, cache: dict = {}, **kwargs):
		chunk_size = kwargs.get("chunk_size", [0, 10, 5])
		encoder_chunk_look_back = kwargs.get("encoder_chunk_look_back", 0)
		decoder_chunk_look_back = kwargs.get("decoder_chunk_look_back", 0)
		batch_size = 1

		enc_output_size = kwargs["encoder_conf"]["output_size"]
		feats_dims = kwargs["frontend_conf"]["n_mels"] * kwargs["frontend_conf"]["lfr_m"]
		cache_encoder = {"start_idx": 0, "cif_hidden": torch.zeros((batch_size, 1, enc_output_size)),
		            "cif_alphas": torch.zeros((batch_size, 1)), "chunk_size": chunk_size,
		            "encoder_chunk_look_back": encoder_chunk_look_back, "last_chunk": False, "opt": None,
		            "feats": torch.zeros((batch_size, chunk_size[0] + chunk_size[2], feats_dims)),
		            "tail_chunk": False}
		cache["encoder"] = cache_encoder
		
		cache_decoder = {"decode_fsmn": None, "decoder_chunk_look_back": decoder_chunk_look_back, "opt": None,
		            "chunk_size": chunk_size}
		cache["decoder"] = cache_decoder
		cache["frontend"] = {}
		cache["prev_samples"] = torch.empty(0)
		
		return cache
	
	def generate_chunk(self,
	                   speech,
	                   speech_lengths=None,
	                   key: list = None,
	                   tokenizer=None,
	                   frontend=None,
	                   **kwargs,
	                   ):
		cache = kwargs.get("cache", {})
		speech.to(device=kwargs["device"]), speech_lengths.to(device=kwargs["device"])
		
		# Encoder
		encoder_out, encoder_out_lens = self.encode_chunk(speech, speech_lengths, cache=cache, is_final=kwargs.get("is_final", False))
		if isinstance(encoder_out, tuple):
			encoder_out = encoder_out[0]
		
		# predictor
		predictor_outs = self.calc_predictor_chunk(encoder_out, encoder_out_lens, cache=cache, is_final=kwargs.get("is_final", False))
		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_chunk(encoder_out,
		                                                     encoder_out_lens,
		                                                     pre_acoustic_embeds,
		                                                     pre_token_length,
		                                                     cache=cache
		                                                     )
		decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]

		results = []
		b, n, d = decoder_out.size()
		if isinstance(key[0], (list, tuple)):
			key = key[0]
		for i in range(b):
			x = encoder_out[i, :encoder_out_lens[i], :]
			am_scores = decoder_out[i, :pre_token_length[i], :]
			if self.beam_search is not None:
				nbest_hyps = self.beam_search(
					x=x, am_scores=am_scores, maxlenratio=kwargs.get("maxlenratio", 0.0),
					minlenratio=kwargs.get("minlenratio", 0.0)
				)
				
				nbest_hyps = nbest_hyps[: self.nbest]
			else:
				
				yseq = am_scores.argmax(dim=-1)
				score = am_scores.max(dim=-1)[0]
				score = torch.sum(score, dim=-1)
				# pad with mask tokens to ensure compatibility with sos/eos tokens
				yseq = torch.tensor(
					[self.sos] + yseq.tolist() + [self.eos], device=yseq.device
				)
				nbest_hyps = [Hypothesis(yseq=yseq, score=score)]
			for nbest_idx, hyp in enumerate(nbest_hyps):
				
				# 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)
				
				result_i = token


				results.extend(result_i)
		
		return results
	
	def generate(self,
	             data_in,
	             data_lengths=None,
	             key: list = None,
	             tokenizer=None,
	             frontend=None,
	             cache: dict={},
	             **kwargs,
	             ):

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

		if len(cache) == 0:
			self.init_cache(cache, **kwargs)
		_is_final = kwargs.get("is_final", False)
		
		meta_data = {}
		chunk_size = kwargs.get("chunk_size", [0, 10, 5])
		chunk_stride_samples = chunk_size[1] * 960  # 600ms
		
		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}"
		assert len(audio_sample_list) == 1, "batch_size must be set 1"
		
		audio_sample = torch.cat((cache["prev_samples"], audio_sample_list[0]))
		
		n = len(audio_sample) // chunk_stride_samples + int(_is_final)
		m = len(audio_sample) % chunk_stride_samples * (1-int(_is_final))
		tokens = []
		for i in range(n):
			kwargs["is_final"] = _is_final and i == n -1
			audio_sample_i = audio_sample[i*chunk_stride_samples:(i+1)*chunk_stride_samples]

			# extract fbank feats
			speech, speech_lengths = extract_fbank([audio_sample_i], data_type=kwargs.get("data_type", "sound"),
			                                       frontend=frontend, cache=cache["frontend"], is_final=kwargs["is_final"])
			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
			
			tokens_i = self.generate_chunk(speech, speech_lengths, key=key, tokenizer=tokenizer, cache=cache, frontend=frontend, **kwargs)
			tokens.extend(tokens_i)
			
		text_postprocessed, _ = postprocess_utils.sentence_postprocess(tokens)
		
		result_i = {"key": key[0], "text": text_postprocessed}
		result = [result_i]
		
		
		cache["prev_samples"] = audio_sample[:-m]
		if _is_final:
			self.init_cache(cache, **kwargs)
		
		if kwargs.get("output_dir"):
			writer = DatadirWriter(kwargs.get("output_dir"))
			ibest_writer = writer[f"{1}best_recog"]
			ibest_writer["token"][key[0]] = " ".join(tokens)
			ibest_writer["text"][key[0]] = text_postprocessed
		
		return result, meta_data