funasr_detach/models/paraformer_streaming/model.py

#!/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 time
import torch
import logging
from typing import Dict, Tuple
from contextlib import contextmanager
from distutils.version import LooseVersion

from funasr_detach.register import tables
from funasr_detach.models.ctc.ctc import CTC
from funasr_detach.utils import postprocess_utils
from funasr_detach.metrics.compute_acc import th_accuracy
from funasr_detach.utils.datadir_writer import DatadirWriter
from funasr_detach.models.paraformer.model import Paraformer
from funasr_detach.models.paraformer.search import Hypothesis
from funasr_detach.models.paraformer.cif_predictor import mae_loss
from funasr_detach.train_utils.device_funcs import force_gatherable
from funasr_detach.losses.label_smoothing_loss import LabelSmoothingLoss
from funasr_detach.models.transformer.utils.add_sos_eos import add_sos_eos
from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask, pad_list
from funasr_detach.utils.load_utils import load_audio_text_image_video, extract_fbank


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


@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_detach.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 = speech.to(device=kwargs["device"])
        speech_lengths = 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)
        )
        print(speech.shape, encoder_out.shape, encoder_out_lens)
        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 inference(
        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)

        meta_data = {}
        chunk_size = kwargs.get("chunk_size", [0, 10, 5])
        chunk_stride_samples = int(chunk_size[1] * 960)  # 600ms

        time1 = time.perf_counter()
        cfg = {"is_final": kwargs.get("is_final", False)}
        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,
            cache=cfg,
        )
        # import pdb; pdb.set_trace()
        _is_final = cfg["is_final"]  # if data_in is a file or url, set is_final=True

        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 = int(len(audio_sample) // chunk_stride_samples + int(_is_final))
        m = int(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
            )
            if len(speech) == 0:
                break
            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"):
            if not hasattr(self, "writer"):
                self.writer = DatadirWriter(kwargs.get("output_dir"))
            ibest_writer = self.writer[f"{1}best_recog"]
            ibest_writer["token"][key[0]] = " ".join(tokens)
            ibest_writer["text"][key[0]] = text_postprocessed

        return result, meta_data

    def infer_encoder(
        self,
        data_in,
        data_lengths=None,
        key: list = None,
        tokenizer=None,
        frontend=None,
        cache: dict = {},
        **kwargs,
    ):
        if len(cache) == 0:
            self.init_cache(cache, **kwargs)

        meta_data = {}
        chunk_size = kwargs.get("chunk_size", [0, 10, 5])
        chunk_stride_samples = int(chunk_size[1] * 960)  # 600ms

        time1 = time.perf_counter()
        cfg = {"is_final": kwargs.get("is_final", False)}
        if isinstance(data_in[0], torch.Tensor):
            audio_sample_list = data_in
        else:
            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,
                cache=cfg,
            )

        _is_final = cfg["is_final"]  # if data_in is a file or url, set is_final=True
        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 = int(len(audio_sample) // chunk_stride_samples + int(_is_final))
        m = int(len(audio_sample) % chunk_stride_samples * (1 - int(_is_final)))
        encoder_outs = []
        meta_data["batch_data_time"] = 0.0
        meta_data["extract_feat"] = 0.0
        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
            ]
            time2 = time.perf_counter()
            # extract fbank feats
            if kwargs["is_final"] and len(audio_sample_i) == 0:
                break
            try:
                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"],
                )
            except:
                if i == n - 1 and audio_sample_i.shape[0] < 480:
                    print(f"Warning!!!, skip {audio_sample_i.shape[0]} samples")
                    break
                else:
                    raise RuntimeError("infer failed")
            time3 = time.perf_counter()
            if len(speech) == 0 and kwargs["is_final"]:
                break
            meta_data["extract_feat"] = meta_data["extract_feat"] + time3 - time2
            meta_data["batch_data_time"] = (
                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_out, encoder_out_lens = self.encode_chunk(
                speech,
                speech_lengths,
                cache=cache,
                is_final=kwargs.get("is_final", False),
            )
            encoder_outs.append(encoder_out[:, (-speech_lengths[0]) :])

            if i == n - 1:
                break
        speech_out = []
        if len(encoder_outs) > 0:
            speech_out = torch.cat(encoder_outs, dim=1)
        result_i = {"key": key[0], "enc_out": speech_out}
        result = [result_i]

        if m > 0:  # tail exists
            cache["prev_samples"] = audio_sample[-m:]
        else:
            cache["prev_samples"] = torch.empty(0)

        if _is_final:
            self.init_cache(cache, **kwargs)

        return result, meta_data, cache