rnnt

aky15

2023-03-14 fc606ceef3aa5a1dbca795a43147c0aa9ddf0b34

 funasr/bin/asr_inference_rnnt.py

@@ -1,151 +1,145 @@
#!/usr/bin/env python3

""" Inference class definition for Transducer models."""

from __future__ import annotations

import argparse
import logging
import math
import sys
import time
import copy
import os
import codecs
import tempfile
import requests
from pathlib import Path
from typing import Optional
from typing import Sequence
from typing import Tuple
from typing import Union
from typing import Dict
from typing import Any
from typing import List
from typing import Any, Dict, List, Optional, Sequence, Tuple, Union

import numpy as np
import torch
from typeguard import check_argument_types
from packaging.version import parse as V
from typeguard import check_argument_types, check_return_type

from funasr.models_transducer.beam_search_transducer import (
    BeamSearchTransducer,
    Hypothesis,
)
from funasr.models_transducer.utils import TooShortUttError
from funasr.fileio.datadir_writer import DatadirWriter
from funasr.modules.beam_search.beam_search import BeamSearchPara as BeamSearch
from funasr.modules.beam_search.beam_search import Hypothesis
from funasr.modules.scorers.ctc import CTCPrefixScorer
from funasr.modules.scorers.length_bonus import LengthBonus
from funasr.modules.subsampling import TooShortUttError
from funasr.tasks.asr import ASRTaskParaformer as ASRTask
from funasr.tasks.asr_transducer import ASRTransducerTask
from funasr.tasks.lm import LMTask
from funasr.text.build_tokenizer import build_tokenizer
from funasr.text.token_id_converter import TokenIDConverter
from funasr.torch_utils.device_funcs import to_device
from funasr.torch_utils.set_all_random_seed import set_all_random_seed
from funasr.utils import config_argparse
from funasr.utils.types import str2bool, str2triple_str, str_or_none
from funasr.utils.cli_utils import get_commandline_args
from funasr.utils.types import str2bool
from funasr.utils.types import str2triple_str
from funasr.utils.types import str_or_none
from funasr.utils import asr_utils, wav_utils, postprocess_utils
from funasr.models.frontend.wav_frontend import WavFrontend
from funasr.models.e2e_asr_paraformer import BiCifParaformer, ContextualParaformer
from funasr.export.models.e2e_asr_paraformer import Paraformer as Paraformer_export


class Speech2Text:
    """Speech2Text class

    Examples:
            >>> import soundfile
            >>> speech2text = Speech2Text("asr_config.yml", "asr.pth")
            >>> audio, rate = soundfile.read("speech.wav")
            >>> speech2text(audio)
            [(text, token, token_int, hypothesis object), ...]

    """Speech2Text class for Transducer models.
    Args:
        asr_train_config: ASR model training config path.
        asr_model_file: ASR model path.
        beam_search_config: Beam search config path.
        lm_train_config: Language Model training config path.
        lm_file: Language Model config path.
        token_type: Type of token units.
        bpemodel: BPE model path.
        device: Device to use for inference.
        beam_size: Size of beam during search.
        dtype: Data type.
        lm_weight: Language model weight.
        quantize_asr_model: Whether to apply dynamic quantization to ASR model.
        quantize_modules: List of module names to apply dynamic quantization on.
        quantize_dtype: Dynamic quantization data type.
        nbest: Number of final hypothesis.
        streaming: Whether to perform chunk-by-chunk inference.
        chunk_size: Number of frames in chunk AFTER subsampling.
        left_context: Number of frames in left context AFTER subsampling.
        right_context: Number of frames in right context AFTER subsampling.
        display_partial_hypotheses: Whether to display partial hypotheses.
    """

    def __init__(
            self,
            asr_train_config: Union[Path, str] = None,
            asr_model_file: Union[Path, str] = None,
            cmvn_file: Union[Path, str] = None,
            lm_train_config: Union[Path, str] = None,
            lm_file: Union[Path, str] = None,
            token_type: str = None,
            bpemodel: str = None,
            device: str = "cpu",
            maxlenratio: float = 0.0,
            minlenratio: float = 0.0,
            dtype: str = "float32",
            beam_size: int = 20,
            ctc_weight: float = 0.5,
            lm_weight: float = 1.0,
            ngram_weight: float = 0.9,
            penalty: float = 0.0,
            nbest: int = 1,
            frontend_conf: dict = None,
            hotword_list_or_file: str = None,
            **kwargs,
    ):
        self,
        asr_train_config: Union[Path, str] = None,
        asr_model_file: Union[Path, str] = None,
        beam_search_config: Dict[str, Any] = None,
        lm_train_config: Union[Path, str] = None,
        lm_file: Union[Path, str] = None,
        token_type: str = None,
        bpemodel: str = None,
        device: str = "cpu",
        beam_size: int = 5,
        dtype: str = "float32",
        lm_weight: float = 1.0,
        quantize_asr_model: bool = False,
        quantize_modules: List[str] = None,
        quantize_dtype: str = "qint8",
        nbest: int = 1,
        streaming: bool = False,
        simu_streaming: bool = False,
        chunk_size: int = 16,
        left_context: int = 32,
        right_context: int = 0,
        display_partial_hypotheses: bool = False,
    ) -> None:
        """Construct a Speech2Text object."""
        super().__init__()

        assert check_argument_types()

        # 1. Build ASR model
        scorers = {}
        asr_model, asr_train_args = ASRTask.build_model_from_file(
            asr_train_config, asr_model_file, cmvn_file, device
        )
        frontend = None
        if asr_train_args.frontend is not None and asr_train_args.frontend_conf is not None:
            frontend = WavFrontend(cmvn_file=cmvn_file, **asr_train_args.frontend_conf)

        logging.info("asr_model: {}".format(asr_model))
        logging.info("asr_train_args: {}".format(asr_train_args))
        asr_model.to(dtype=getattr(torch, dtype)).eval()

        if asr_model.ctc != None:
            ctc = CTCPrefixScorer(ctc=asr_model.ctc, eos=asr_model.eos)
            scorers.update(
                ctc=ctc
            )
        token_list = asr_model.token_list
        scorers.update(
            length_bonus=LengthBonus(len(token_list)),
        asr_model, asr_train_args = ASRTransducerTask.build_model_from_file(
            asr_train_config, asr_model_file, device
        )

        # 2. Build Language model
        if quantize_asr_model:
            if quantize_modules is not None:
                if not all([q in ["LSTM", "Linear"] for q in quantize_modules]):
                    raise ValueError(
                        "Only 'Linear' and 'LSTM' modules are currently supported"
                        " by PyTorch and in --quantize_modules"
                    )

                q_config = set([getattr(torch.nn, q) for q in quantize_modules])
            else:
                q_config = {torch.nn.Linear}

            if quantize_dtype == "float16" and (V(torch.__version__) < V("1.5.0")):
                raise ValueError(
                    "float16 dtype for dynamic quantization is not supported with torch"
                    " version < 1.5.0. Switching to qint8 dtype instead."
                )
            q_dtype = getattr(torch, quantize_dtype)

            asr_model = torch.quantization.quantize_dynamic(
                asr_model, q_config, dtype=q_dtype
            ).eval()
        else:
            asr_model.to(dtype=getattr(torch, dtype)).eval()

        if lm_train_config is not None:
            lm, lm_train_args = LMTask.build_model_from_file(
                lm_train_config, lm_file, device
            )
            scorers["lm"] = lm.lm

        # 3. Build ngram model
        # ngram is not supported now
        ngram = None
        scorers["ngram"] = ngram
            lm_scorer = lm.lm
        else:
            lm_scorer = None

        # 4. Build BeamSearch object
        # transducer is not supported now
        beam_search_transducer = None
        if beam_search_config is None:
            beam_search_config = {}

        weights = dict(
            decoder=1.0 - ctc_weight,
            ctc=ctc_weight,
            lm=lm_weight,
            ngram=ngram_weight,
            length_bonus=penalty,
        )
        beam_search = BeamSearch(
            beam_size=beam_size,
            weights=weights,
            scorers=scorers,
            sos=asr_model.sos,
            eos=asr_model.eos,
            vocab_size=len(token_list),
            token_list=token_list,
            pre_beam_score_key=None if ctc_weight == 1.0 else "full",
        beam_search = BeamSearchTransducer(
            asr_model.decoder,
            asr_model.joint_network,
            beam_size,
            lm=lm_scorer,
            lm_weight=lm_weight,
            nbest=nbest,
            **beam_search_config,
        )

        beam_search.to(device=device, dtype=getattr(torch, dtype)).eval()
        for scorer in scorers.values():
            if isinstance(scorer, torch.nn.Module):
                scorer.to(device=device, dtype=getattr(torch, dtype)).eval()
        token_list = asr_model.token_list

        logging.info(f"Decoding device={device}, dtype={dtype}")

        # 5. [Optional] Build Text converter: e.g. bpe-sym -> Text
        if token_type is None:
            token_type = asr_train_args.token_type
        if bpemodel is None:
@@ -165,439 +159,397 @@

        self.asr_model = asr_model
        self.asr_train_args = asr_train_args
        self.device = device
        self.dtype = dtype
        self.nbest = nbest

        self.converter = converter
        self.tokenizer = tokenizer

        # 6. [Optional] Build hotword list from str, local file or url
        self.hotword_list = None
        self.hotword_list = self.generate_hotwords_list(hotword_list_or_file)

        is_use_lm = lm_weight != 0.0 and lm_file is not None
        if (ctc_weight == 0.0 or asr_model.ctc == None) and not is_use_lm:
            beam_search = None
        self.beam_search = beam_search
        logging.info(f"Beam_search: {self.beam_search}")
        self.beam_search_transducer = beam_search_transducer
        self.maxlenratio = maxlenratio
        self.minlenratio = minlenratio
        self.device = device
        self.dtype = dtype
        self.nbest = nbest
        self.frontend = frontend
        self.encoder_downsampling_factor = 1
        if asr_train_args.encoder == "data2vec_encoder" or asr_train_args.encoder_conf["input_layer"] == "conv2d":
            self.encoder_downsampling_factor = 4
        self.streaming = streaming
        self.simu_streaming = simu_streaming
        self.chunk_size = max(chunk_size, 0)
        self.left_context = max(left_context, 0)
        self.right_context = max(right_context, 0)

    @torch.no_grad()
    def __call__(
            self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None
    ):
        """Inference
        if not streaming or chunk_size == 0:
            self.streaming = False
            self.asr_model.encoder.dynamic_chunk_training = False
        
        if not simu_streaming or chunk_size == 0:
            self.simu_streaming = False
            self.asr_model.encoder.dynamic_chunk_training = False

        Args:
                speech: Input speech data
        Returns:
                text, token, token_int, hyp
        self.n_fft = asr_train_args.frontend_conf.get("n_fft", 512)
        self.hop_length = asr_train_args.frontend_conf.get("hop_length", 128)

        """
        assert check_argument_types()

        # Input as audio signal
        if isinstance(speech, np.ndarray):
            speech = torch.tensor(speech)

        if self.frontend is not None:
            feats, feats_len = self.frontend.forward(speech, speech_lengths)
            feats = to_device(feats, device=self.device)
            feats_len = feats_len.int()
            self.asr_model.frontend = None
        if asr_train_args.frontend_conf.get("win_length", None) is not None:
            self.frontend_window_size = asr_train_args.frontend_conf["win_length"]
        else:
            feats = speech
            feats_len = speech_lengths
        lfr_factor = max(1, (feats.size()[-1] // 80) - 1)
        batch = {"speech": feats, "speech_lengths": feats_len}
            self.frontend_window_size = self.n_fft

        # a. To device
        batch = to_device(batch, device=self.device)

        # b. Forward Encoder
        enc, enc_len = self.asr_model.encode(**batch)
        if isinstance(enc, tuple):
            enc = enc[0]
        # assert len(enc) == 1, len(enc)
        enc_len_batch_total = torch.sum(enc_len).item() * self.encoder_downsampling_factor

        predictor_outs = self.asr_model.calc_predictor(enc, enc_len)
        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 []
        if not isinstance(self.asr_model, ContextualParaformer):
            if self.hotword_list:
                logging.warning("Hotword is given but asr model is not a ContextualParaformer.")
            decoder_outs = self.asr_model.cal_decoder_with_predictor(enc, enc_len, pre_acoustic_embeds, pre_token_length)
            decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
        else:
            decoder_outs = self.asr_model.cal_decoder_with_predictor(enc, enc_len, pre_acoustic_embeds, pre_token_length, hw_list=self.hotword_list)
            decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]

        results = []
        b, n, d = decoder_out.size()
        for i in range(b):
            x = enc[i, :enc_len[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=self.maxlenratio, minlenratio=self.minlenratio
                )

                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.asr_model.sos] + yseq.tolist() + [self.asr_model.eos], device=yseq.device
                )
                nbest_hyps = [Hypothesis(yseq=yseq, score=score)]

            for hyp in nbest_hyps:
                assert isinstance(hyp, (Hypothesis)), type(hyp)

                # 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 != 0 and x != 2, token_int))

                # Change integer-ids to tokens
                token = self.converter.ids2tokens(token_int)

                if self.tokenizer is not None:
                    text = self.tokenizer.tokens2text(token)
                else:
                    text = None

                results.append((text, token, token_int, hyp, enc_len_batch_total, lfr_factor))

        # assert check_return_type(results)
        return results

    def generate_hotwords_list(self, hotword_list_or_file):
        # for None
        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'):
            logging.info("Attempting to parse hotwords from local txt...")
            hotword_list = []
            hotword_str_list = []
            with codecs.open(hotword_list_or_file, 'r') as fin:
                for line in fin.readlines():
                    hw = line.strip()
                    hotword_str_list.append(hw)
                    hotword_list.append(self.converter.tokens2ids([i for i in hw]))
                hotword_list.append([self.asr_model.sos])
                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'):
            logging.info("Attempting to parse hotwords from url...")
            work_dir = tempfile.TemporaryDirectory().name
            if not os.path.exists(work_dir):
                os.makedirs(work_dir)
            text_file_path = os.path.join(work_dir, os.path.basename(hotword_list_or_file))
            local_file = requests.get(hotword_list_or_file)
            open(text_file_path, "wb").write(local_file.content)
            hotword_list_or_file = text_file_path
            hotword_list = []
            hotword_str_list = []
            with codecs.open(hotword_list_or_file, 'r') as fin:
                for line in fin.readlines():
                    hw = line.strip()
                    hotword_str_list.append(hw)
                    hotword_list.append(self.converter.tokens2ids([i for i in hw]))
                hotword_list.append([self.asr_model.sos])
                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'):
            logging.info("Attempting to parse hotwords as str...")
            hotword_list = []
            hotword_str_list = []
            for hw in hotword_list_or_file.strip().split():
                hotword_str_list.append(hw)
                hotword_list.append(self.converter.tokens2ids([i for i in hw]))
            hotword_list.append([self.asr_model.sos])
            hotword_str_list.append('<s>')
            logging.info("Hotword list: {}.".format(hotword_str_list))
        else:
            hotword_list = None
        return hotword_list

class Speech2TextExport:
    """Speech2TextExport class

    """

    def __init__(
            self,
            asr_train_config: Union[Path, str] = None,
            asr_model_file: Union[Path, str] = None,
            cmvn_file: Union[Path, str] = None,
            lm_train_config: Union[Path, str] = None,
            lm_file: Union[Path, str] = None,
            token_type: str = None,
            bpemodel: str = None,
            device: str = "cpu",
            maxlenratio: float = 0.0,
            minlenratio: float = 0.0,
            dtype: str = "float32",
            beam_size: int = 20,
            ctc_weight: float = 0.5,
            lm_weight: float = 1.0,
            ngram_weight: float = 0.9,
            penalty: float = 0.0,
            nbest: int = 1,
            frontend_conf: dict = None,
            hotword_list_or_file: str = None,
            **kwargs,
    ):

        # 1. Build ASR model
        asr_model, asr_train_args = ASRTask.build_model_from_file(
            asr_train_config, asr_model_file, cmvn_file, device
        self.window_size = self.chunk_size + self.right_context
        self._raw_ctx = self.asr_model.encoder.get_encoder_input_raw_size(
            self.window_size, self.hop_length
        )
        frontend = None
        if asr_train_args.frontend is not None and asr_train_args.frontend_conf is not None:
            frontend = WavFrontend(cmvn_file=cmvn_file, **asr_train_args.frontend_conf)
        self._ctx = self.asr_model.encoder.get_encoder_input_size(
            self.window_size
        )
       

        logging.info("asr_model: {}".format(asr_model))
        logging.info("asr_train_args: {}".format(asr_train_args))
        asr_model.to(dtype=getattr(torch, dtype)).eval()
        #self.last_chunk_length = (
        #    self.asr_model.encoder.embed.min_frame_length + self.right_context + 1
        #) * self.hop_length

        token_list = asr_model.token_list
        self.last_chunk_length = (
            self.asr_model.encoder.embed.min_frame_length + self.right_context + 1
        )
        self.reset_inference_cache()

    def reset_inference_cache(self) -> None:
        """Reset Speech2Text parameters."""
        self.frontend_cache = None

        self.asr_model.encoder.reset_streaming_cache(
            self.left_context, device=self.device
        )
        self.beam_search.reset_inference_cache()

        logging.info(f"Decoding device={device}, dtype={dtype}")
        self.num_processed_frames = torch.tensor([[0]], device=self.device)

        # 5. [Optional] Build Text converter: e.g. bpe-sym -> Text
        if token_type is None:
            token_type = asr_train_args.token_type
        if bpemodel is None:
            bpemodel = asr_train_args.bpemodel

        if token_type is None:
            tokenizer = None
        elif token_type == "bpe":
            if bpemodel is not None:
                tokenizer = build_tokenizer(token_type=token_type, bpemodel=bpemodel)
            else:
                tokenizer = None
        else:
            tokenizer = build_tokenizer(token_type=token_type)
        converter = TokenIDConverter(token_list=token_list)
        logging.info(f"Text tokenizer: {tokenizer}")

        # self.asr_model = asr_model
        self.asr_train_args = asr_train_args
        self.converter = converter
        self.tokenizer = tokenizer

        self.device = device
        self.dtype = dtype
        self.nbest = nbest
        self.frontend = frontend

        model = Paraformer_export(asr_model, onnx=False)
        self.asr_model = model
        
    @torch.no_grad()
    def __call__(
            self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None
    ):
        """Inference

    def apply_frontend(
        self, speech: torch.Tensor, is_final: bool = False
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """Forward frontend.
        Args:
                speech: Input speech data
            speech: Speech data. (S)
            is_final: Whether speech corresponds to the final (or only) chunk of data.
        Returns:
                text, token, token_int, hyp
            feats: Features sequence. (1, T_in, F)
            feats_lengths: Features sequence length. (1, T_in, F)
        """
        if self.frontend_cache is not None:
            speech = torch.cat([self.frontend_cache["waveform_buffer"], speech], dim=0)

        if is_final:
            if self.streaming and speech.size(0) < self.last_chunk_length:
                pad = torch.zeros(
                    self.last_chunk_length - speech.size(0), dtype=speech.dtype
                )
                speech = torch.cat([speech, pad], dim=0)

            speech_to_process = speech
            waveform_buffer = None
        else:
            n_frames = (
                speech.size(0) - (self.frontend_window_size - self.hop_length)
            ) // self.hop_length

            n_residual = (
                speech.size(0) - (self.frontend_window_size - self.hop_length)
            ) % self.hop_length

            speech_to_process = speech.narrow(
                0,
                0,
                (self.frontend_window_size - self.hop_length)
                + n_frames * self.hop_length,
            )

            waveform_buffer = speech.narrow(
                0,
                speech.size(0)
                - (self.frontend_window_size - self.hop_length)
                - n_residual,
                (self.frontend_window_size - self.hop_length) + n_residual,
            ).clone()

        speech_to_process = speech_to_process.unsqueeze(0).to(
            getattr(torch, self.dtype)
        )
        lengths = speech_to_process.new_full(
            [1], dtype=torch.long, fill_value=speech_to_process.size(1)
        )
        batch = {"speech": speech_to_process, "speech_lengths": lengths}
        batch = to_device(batch, device=self.device)

        feats, feats_lengths = self.asr_model._extract_feats(**batch)
        if self.asr_model.normalize is not None:
            feats, feats_lengths = self.asr_model.normalize(feats, feats_lengths)

        if is_final:
            if self.frontend_cache is None:
                pass
            else:
                feats = feats.narrow(
                    1,
                    math.ceil(
                        math.ceil(self.frontend_window_size / self.hop_length) / 2
                    ),
                    feats.size(1)
                    - math.ceil(
                        math.ceil(self.frontend_window_size / self.hop_length) / 2
                    ),
                )
        else:
            if self.frontend_cache is None:
                feats = feats.narrow(
                    1,
                    0,
                    feats.size(1)
                    - math.ceil(
                        math.ceil(self.frontend_window_size / self.hop_length) / 2
                    ),
                )
            else:
                feats = feats.narrow(
                    1,
                    math.ceil(
                        math.ceil(self.frontend_window_size / self.hop_length) / 2
                    ),
                    feats.size(1)
                    - 2
                    * math.ceil(
                        math.ceil(self.frontend_window_size / self.hop_length) / 2
                    ),
                )

        feats_lengths = feats.new_full([1], dtype=torch.long, fill_value=feats.size(1))

        if is_final:
            self.frontend_cache = None
        else:
            self.frontend_cache = {"waveform_buffer": waveform_buffer}

        return feats, feats_lengths

    @torch.no_grad()
    def streaming_decode(
        self,
        speech: Union[torch.Tensor, np.ndarray],
        is_final: bool = True,
    ) -> List[Hypothesis]:
        """Speech2Text streaming call.
        Args:
            speech: Chunk of speech data. (S)
            is_final: Whether speech corresponds to the final chunk of data.
        Returns:
            nbest_hypothesis: N-best hypothesis.
        """
        if isinstance(speech, np.ndarray):
            speech = torch.tensor(speech)
        if is_final:
            if self.streaming and speech.size(0) < self.last_chunk_length:
                pad = torch.zeros(
                    self.last_chunk_length - speech.size(0), speech.size(1),  dtype=speech.dtype
                )
                speech = torch.cat([speech, pad], dim=0) #feats, feats_length = self.apply_frontend(speech, is_final=is_final)

        feats = speech.unsqueeze(0).to(getattr(torch, self.dtype))
        feats_lengths = feats.new_full([1], dtype=torch.long, fill_value=feats.size(1))

        if self.asr_model.normalize is not None:
            feats, feats_lengths = self.asr_model.normalize(feats, feats_lengths)

        feats = to_device(feats, device=self.device)
        feats_lengths = to_device(feats_lengths, device=self.device)
        enc_out = self.asr_model.encoder.chunk_forward(
            feats,
            feats_lengths,
            self.num_processed_frames,
            chunk_size=self.chunk_size,
            left_context=self.left_context,
            right_context=self.right_context,
        )
        nbest_hyps = self.beam_search(enc_out[0], is_final=is_final)

        self.num_processed_frames += self.chunk_size

        if is_final:
            self.reset_inference_cache()

        return nbest_hyps

    @torch.no_grad()
    def simu_streaming_decode(self, speech: Union[torch.Tensor, np.ndarray]) -> List[Hypothesis]:
        """Speech2Text call.
        Args:
            speech: Speech data. (S)
        Returns:
            nbest_hypothesis: N-best hypothesis.
        """
        assert check_argument_types()

        # Input as audio signal
        if isinstance(speech, np.ndarray):
            speech = torch.tensor(speech)

        if self.frontend is not None:
            feats, feats_len = self.frontend.forward(speech, speech_lengths)
            feats = to_device(feats, device=self.device)
            feats_len = feats_len.int()
            self.asr_model.frontend = None
        else:
            feats = speech
            feats_len = speech_lengths

        enc_len_batch_total = feats_len.sum()
        lfr_factor = max(1, (feats.size()[-1] // 80) - 1)
        batch = {"speech": feats, "speech_lengths": feats_len}

        # a. To device
        batch = to_device(batch, device=self.device)

        decoder_outs = self.asr_model(**batch)
        decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
        
        feats = speech.unsqueeze(0).to(getattr(torch, self.dtype))
        feats_lengths = feats.new_full([1], dtype=torch.long, fill_value=feats.size(1))

        if self.asr_model.normalize is not None:
            feats, feats_lengths = self.asr_model.normalize(feats, feats_lengths)

        feats = to_device(feats, device=self.device)
        feats_lengths = to_device(feats_lengths, device=self.device)
        enc_out = self.asr_model.encoder.simu_chunk_forward(feats, feats_lengths, self.chunk_size, self.left_context, self.right_context)
        nbest_hyps = self.beam_search(enc_out[0])

        return nbest_hyps

    @torch.no_grad()
    def __call__(self, speech: Union[torch.Tensor, np.ndarray]) -> List[Hypothesis]:
        """Speech2Text call.
        Args:
            speech: Speech data. (S)
        Returns:
            nbest_hypothesis: N-best hypothesis.
        """
        assert check_argument_types()

        if isinstance(speech, np.ndarray):
            speech = torch.tensor(speech)
        
        # lengths: (1,)
        # feats, feats_length = self.apply_frontend(speech)
        feats = speech.unsqueeze(0).to(getattr(torch, self.dtype))
        # lengths: (1,)
        feats_lengths = feats.new_full([1], dtype=torch.long, fill_value=feats.size(1))

        # print(feats.shape)
        # print(feats_lengths)
        if self.asr_model.normalize is not None:
            feats, feats_lengths = self.asr_model.normalize(feats, feats_lengths)

        feats = to_device(feats, device=self.device)
        feats_lengths = to_device(feats_lengths, device=self.device)

        enc_out, _ = self.asr_model.encoder(feats, feats_lengths)

        nbest_hyps = self.beam_search(enc_out[0])

        return nbest_hyps

    def hypotheses_to_results(self, nbest_hyps: List[Hypothesis]) -> List[Any]:
        """Build partial or final results from the hypotheses.
        Args:
            nbest_hyps: N-best hypothesis.
        Returns:
            results: Results containing different representation for the hypothesis.
        """
        results = []
        b, n, d = decoder_out.size()
        for i in range(b):
            am_scores = decoder_out[i, :ys_pad_lens[i], :]

            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(
                yseq.tolist(), device=yseq.device
            )
            nbest_hyps = [Hypothesis(yseq=yseq, score=score)]
        for hyp in nbest_hyps:
            token_int = list(filter(lambda x: x != 0, hyp.yseq))

            for hyp in nbest_hyps:
                assert isinstance(hyp, (Hypothesis)), type(hyp)
            token = self.converter.ids2tokens(token_int)

                # 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()
            if self.tokenizer is not None:
                text = self.tokenizer.tokens2text(token)
            else:
                text = None
            results.append((text, token, token_int, hyp))

                # remove blank symbol id, which is assumed to be 0
                token_int = list(filter(lambda x: x != 0 and x != 2, token_int))

                # Change integer-ids to tokens
                token = self.converter.ids2tokens(token_int)

                if self.tokenizer is not None:
                    text = self.tokenizer.tokens2text(token)
                else:
                    text = None

                results.append((text, token, token_int, hyp, enc_len_batch_total, lfr_factor))
            assert check_return_type(results)

        return results

    @staticmethod
    def from_pretrained(
        model_tag: Optional[str] = None,
        **kwargs: Optional[Any],
    ) -> Speech2Text:
        """Build Speech2Text instance from the pretrained model.
        Args:
            model_tag: Model tag of the pretrained models.
        Return:
            : Speech2Text instance.
        """
        if model_tag is not None:
            try:
                from espnet_model_zoo.downloader import ModelDownloader

            except ImportError:
                logging.error(
                    "`espnet_model_zoo` is not installed. "
                    "Please install via `pip install -U espnet_model_zoo`."
                )
                raise
            d = ModelDownloader()
            kwargs.update(**d.download_and_unpack(model_tag))

        return Speech2Text(**kwargs)


def inference(
        maxlenratio: float,
        minlenratio: float,
        batch_size: int,
        beam_size: int,
        ngpu: int,
        ctc_weight: float,
        lm_weight: float,
        penalty: float,
        log_level: Union[int, str],
        data_path_and_name_and_type,
        asr_train_config: Optional[str],
        asr_model_file: Optional[str],
        cmvn_file: Optional[str] = None,
        raw_inputs: Union[np.ndarray, torch.Tensor] = None,
        lm_train_config: Optional[str] = None,
        lm_file: Optional[str] = None,
        token_type: Optional[str] = None,
        key_file: Optional[str] = None,
        word_lm_train_config: Optional[str] = None,
        bpemodel: Optional[str] = None,
        allow_variable_data_keys: bool = False,
        streaming: bool = False,
        output_dir: Optional[str] = None,
        dtype: str = "float32",
        seed: int = 0,
        ngram_weight: float = 0.9,
        nbest: int = 1,
        num_workers: int = 1,

        **kwargs,
):
    inference_pipeline = inference_modelscope(
        maxlenratio=maxlenratio,
        minlenratio=minlenratio,
        batch_size=batch_size,
        beam_size=beam_size,
        ngpu=ngpu,
        ctc_weight=ctc_weight,
        lm_weight=lm_weight,
        penalty=penalty,
        log_level=log_level,
        asr_train_config=asr_train_config,
        asr_model_file=asr_model_file,
        cmvn_file=cmvn_file,
        raw_inputs=raw_inputs,
        lm_train_config=lm_train_config,
        lm_file=lm_file,
        token_type=token_type,
        key_file=key_file,
        word_lm_train_config=word_lm_train_config,
        bpemodel=bpemodel,
        allow_variable_data_keys=allow_variable_data_keys,
        streaming=streaming,
        output_dir=output_dir,
        dtype=dtype,
        seed=seed,
        ngram_weight=ngram_weight,
        nbest=nbest,
        num_workers=num_workers,

        **kwargs,
    )
    return inference_pipeline(data_path_and_name_and_type, raw_inputs)


def inference_modelscope(
        maxlenratio: float,
        minlenratio: float,
        batch_size: int,
        beam_size: int,
        ngpu: int,
        ctc_weight: float,
        lm_weight: float,
        penalty: float,
        log_level: Union[int, str],
        # data_path_and_name_and_type,
        asr_train_config: Optional[str],
        asr_model_file: Optional[str],
        cmvn_file: Optional[str] = None,
        lm_train_config: Optional[str] = None,
        lm_file: Optional[str] = None,
        token_type: Optional[str] = None,
        key_file: Optional[str] = None,
        word_lm_train_config: Optional[str] = None,
        bpemodel: Optional[str] = None,
        allow_variable_data_keys: bool = False,
        dtype: str = "float32",
        seed: int = 0,
        ngram_weight: float = 0.9,
        nbest: int = 1,
        num_workers: int = 1,
        output_dir: Optional[str] = None,
        param_dict: dict = None,
        **kwargs,
):
    output_dir: str,
    batch_size: int,
    dtype: str,
    beam_size: int,
    ngpu: int,
    seed: int,
    lm_weight: float,
    nbest: int,
    num_workers: int,
    log_level: Union[int, str],
    data_path_and_name_and_type: Sequence[Tuple[str, str, str]],
    asr_train_config: Optional[str],
    asr_model_file: Optional[str],
    beam_search_config: Optional[dict],
    lm_train_config: Optional[str],
    lm_file: Optional[str],
    model_tag: Optional[str],
    token_type: Optional[str],
    bpemodel: Optional[str],
    key_file: Optional[str],
    allow_variable_data_keys: bool,
    quantize_asr_model: Optional[bool],
    quantize_modules: Optional[List[str]],
    quantize_dtype: Optional[str],
    streaming: Optional[bool],
    simu_streaming: Optional[bool],
    chunk_size: Optional[int],
    left_context: Optional[int],
    right_context: Optional[int],
    display_partial_hypotheses: bool,
    **kwargs,
) -> None:
    """Transducer model inference.
    Args:
        output_dir: Output directory path.
        batch_size: Batch decoding size.
        dtype: Data type.
        beam_size: Beam size.
        ngpu: Number of GPUs.
        seed: Random number generator seed.
        lm_weight: Weight of language model.
        nbest: Number of final hypothesis.
        num_workers: Number of workers.
        log_level: Level of verbose for logs.
        data_path_and_name_and_type:
        asr_train_config: ASR model training config path.
        asr_model_file: ASR model path.
        beam_search_config: Beam search config path.
        lm_train_config: Language Model training config path.
        lm_file: Language Model path.
        model_tag: Model tag.
        token_type: Type of token units.
        bpemodel: BPE model path.
        key_file: File key.
        allow_variable_data_keys: Whether to allow variable data keys.
        quantize_asr_model: Whether to apply dynamic quantization to ASR model.
        quantize_modules: List of module names to apply dynamic quantization on.
        quantize_dtype: Dynamic quantization data type.
        streaming: Whether to perform chunk-by-chunk inference.
        chunk_size: Number of frames in chunk AFTER subsampling.
        left_context: Number of frames in left context AFTER subsampling.
        right_context: Number of frames in right context AFTER subsampling.
        display_partial_hypotheses: Whether to display partial hypotheses.
    """
    assert check_argument_types()

    if word_lm_train_config is not None:
        raise NotImplementedError("Word LM is not implemented")
    if batch_size > 1:
        raise NotImplementedError("batch decoding is not implemented")
    if ngpu > 1:
        raise NotImplementedError("only single GPU decoding is supported")

@@ -605,19 +557,11 @@
        level=log_level,
        format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
    )
    
    export_mode = False
    if param_dict is not None:
        hotword_list_or_file = param_dict.get('hotword')
        export_mode = param_dict.get("export_mode", False)
    else:
        hotword_list_or_file = None

    if ngpu >= 1 and torch.cuda.is_available():
    if ngpu >= 1:
        device = "cuda"
    else:
        device = "cpu"
        batch_size = 1

    # 1. Set random-seed
    set_all_random_seed(seed)
@@ -626,144 +570,105 @@
    speech2text_kwargs = dict(
        asr_train_config=asr_train_config,
        asr_model_file=asr_model_file,
        cmvn_file=cmvn_file,
        beam_search_config=beam_search_config,
        lm_train_config=lm_train_config,
        lm_file=lm_file,
        token_type=token_type,
        bpemodel=bpemodel,
        device=device,
        maxlenratio=maxlenratio,
        minlenratio=minlenratio,
        dtype=dtype,
        beam_size=beam_size,
        ctc_weight=ctc_weight,
        lm_weight=lm_weight,
        ngram_weight=ngram_weight,
        penalty=penalty,
        nbest=nbest,
        hotword_list_or_file=hotword_list_or_file,
        quantize_asr_model=quantize_asr_model,
        quantize_modules=quantize_modules,
        quantize_dtype=quantize_dtype,
        streaming=streaming,
        simu_streaming=simu_streaming,
        chunk_size=chunk_size,
        left_context=left_context,
        right_context=right_context,
    )
    if export_mode:
        speech2text = Speech2TextExport(**speech2text_kwargs)
    else:
        speech2text = Speech2Text(**speech2text_kwargs)
    speech2text = Speech2Text.from_pretrained(
        model_tag=model_tag,
        **speech2text_kwargs,
    )

    def _forward(
            data_path_and_name_and_type,
            raw_inputs: Union[np.ndarray, torch.Tensor] = None,
            output_dir_v2: Optional[str] = None,
            fs: dict = None,
            param_dict: dict = None,
            **kwargs,
    ):
    # 3. Build data-iterator
    loader = ASRTransducerTask.build_streaming_iterator(
        data_path_and_name_and_type,
        dtype=dtype,
        batch_size=batch_size,
        key_file=key_file,
        num_workers=num_workers,
        preprocess_fn=ASRTransducerTask.build_preprocess_fn(
            speech2text.asr_train_args, False
        ),
        collate_fn=ASRTransducerTask.build_collate_fn(
            speech2text.asr_train_args, False
        ),
        allow_variable_data_keys=allow_variable_data_keys,
        inference=True,
    )

        hotword_list_or_file = None
        if param_dict is not None:
            hotword_list_or_file = param_dict.get('hotword')
        if 'hotword' in kwargs:
            hotword_list_or_file = kwargs['hotword']
        if hotword_list_or_file is not None or 'hotword' in kwargs:
            speech2text.hotword_list = speech2text.generate_hotwords_list(hotword_list_or_file)
        cache = None
        if 'cache' in param_dict:
            cache = param_dict['cache']
        # 3. Build data-iterator
        if data_path_and_name_and_type is None and raw_inputs is not None:
            if isinstance(raw_inputs, torch.Tensor):
                raw_inputs = raw_inputs.numpy()
            data_path_and_name_and_type = [raw_inputs, "speech", "waveform"]
        loader = ASRTask.build_streaming_iterator(
            data_path_and_name_and_type,
            dtype=dtype,
            fs=fs,
            batch_size=batch_size,
            key_file=key_file,
            num_workers=num_workers,
            preprocess_fn=ASRTask.build_preprocess_fn(speech2text.asr_train_args, False),
            collate_fn=ASRTask.build_collate_fn(speech2text.asr_train_args, False),
            allow_variable_data_keys=allow_variable_data_keys,
            inference=True,
        )

        forward_time_total = 0.0
        length_total = 0.0
        finish_count = 0
        file_count = 1
        # 7 .Start for-loop
        # FIXME(kamo): The output format should be discussed about
        asr_result_list = []
        output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
        if output_path is not None:
            writer = DatadirWriter(output_path)
        else:
            writer = None

    # 4 .Start for-loop
    with DatadirWriter(output_dir) as writer:
        for keys, batch in loader:
            assert isinstance(batch, dict), type(batch)
            assert all(isinstance(s, str) for s in keys), keys

            _bs = len(next(iter(batch.values())))
            assert len(keys) == _bs, f"{len(keys)} != {_bs}"
            # batch = {k: v for k, v in batch.items() if not k.endswith("_lengths")}
            batch = {k: v[0] for k, v in batch.items() if not k.endswith("_lengths")}
            assert len(batch.keys()) == 1

            logging.info("decoding, utt_id: {}".format(keys))
            # N-best list of (text, token, token_int, hyp_object)
            try:
                if speech2text.streaming:
                    speech = batch["speech"]

            time_beg = time.time()
            results = speech2text(cache=cache, **batch)
            if len(results) < 1:
                hyp = Hypothesis(score=0.0, scores={}, states={}, yseq=[])
                results = [[" ", ["sil"], [2], hyp, 10, 6]] * nbest
            time_end = time.time()
            forward_time = time_end - time_beg
            lfr_factor = results[0][-1]
            length = results[0][-2]
            forward_time_total += forward_time
            length_total += length
            rtf_cur = "decoding, feature length: {}, forward_time: {:.4f}, rtf: {:.4f}".format(length, forward_time, 100 * forward_time / (length * lfr_factor))
            logging.info(rtf_cur)
                    _steps = len(speech) // speech2text._ctx
                    _end = 0
                    for i in range(_steps):
                        _end = (i + 1) * speech2text._ctx

            for batch_id in range(_bs):
                result = [results[batch_id][:-2]]
                        speech2text.streaming_decode(
                            speech[i * speech2text._ctx : _end], is_final=False
                        )

                key = keys[batch_id]
                for n, (text, token, token_int, hyp) in zip(range(1, nbest + 1), result):
                    # Create a directory: outdir/{n}best_recog
                    if writer is not None:
                        ibest_writer = writer[f"{n}best_recog"]
                    final_hyps = speech2text.streaming_decode(
                        speech[_end : len(speech)], is_final=True
                    )
                elif speech2text.simu_streaming:
                    final_hyps = speech2text.simu_streaming_decode(**batch)
                else:
                    final_hyps = speech2text(**batch)

                        # Write the result to each file
                        ibest_writer["token"][key] = " ".join(token)
                        # ibest_writer["token_int"][key] = " ".join(map(str, token_int))
                        ibest_writer["score"][key] = str(hyp.score)
                        ibest_writer["rtf"][key] = rtf_cur
                results = speech2text.hypotheses_to_results(final_hyps)
            except TooShortUttError as e:
                logging.warning(f"Utterance {keys} {e}")
                hyp = Hypothesis(score=0.0, yseq=[], dec_state=None)
                results = [[" ", ["<space>"], [2], hyp]] * nbest

                    if text is not None:
                        text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
                        item = {'key': key, 'value': text_postprocessed}
                        asr_result_list.append(item)
                        finish_count += 1
                        # asr_utils.print_progress(finish_count / file_count)
                        if writer is not None:
                            ibest_writer["text"][key] = text_postprocessed
            key = keys[0]
            for n, (text, token, token_int, hyp) in zip(range(1, nbest + 1), results):
                ibest_writer = writer[f"{n}best_recog"]

                    logging.info("decoding, utt: {}, predictions: {}".format(key, text))
        rtf_avg = "decoding, feature length total: {}, forward_time total: {:.4f}, rtf avg: {:.4f}".format(length_total, forward_time_total, 100 * forward_time_total / (length_total * lfr_factor))
        logging.info(rtf_avg)
        if writer is not None:
            ibest_writer["rtf"]["rtf_avf"] = rtf_avg
        return asr_result_list
                ibest_writer["token"][key] = " ".join(token)
                ibest_writer["token_int"][key] = " ".join(map(str, token_int))
                ibest_writer["score"][key] = str(hyp.score)

    return _forward
                if text is not None:
                    ibest_writer["text"][key] = text


def get_parser():
    """Get Transducer model inference parser."""

    parser = config_argparse.ArgumentParser(
        description="ASR Decoding",
        description="ASR Transducer Decoding",
        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
    )

    # Note(kamo): Use '_' instead of '-' as separator.
    # '-' is confusing if written in yaml.
    parser.add_argument(
        "--log_level",
        type=lambda x: x.upper(),
@@ -792,17 +697,12 @@
        default=1,
        help="The number of workers used for DataLoader",
    )
    parser.add_argument(
        "--hotword",
        type=str_or_none,
        default=None,
        help="hotword file path or hotwords seperated by space"
    )

    group = parser.add_argument_group("Input data related")
    group.add_argument(
        "--data_path_and_name_and_type",
        type=str2triple_str,
        required=False,
        required=True,
        action="append",
    )
    group.add_argument("--key_file", type=str_or_none)
@@ -820,11 +720,6 @@
        help="ASR model parameter file",
    )
    group.add_argument(
        "--cmvn_file",
        type=str,
        help="Global cmvn file",
    )
    group.add_argument(
        "--lm_train_config",
        type=str,
        help="LM training configuration",
@@ -835,25 +730,10 @@
        help="LM parameter file",
    )
    group.add_argument(
        "--word_lm_train_config",
        type=str,
        help="Word LM training configuration",
    )
    group.add_argument(
        "--word_lm_file",
        type=str,
        help="Word LM parameter file",
    )
    group.add_argument(
        "--ngram_file",
        type=str,
        help="N-gram parameter file",
    )
    group.add_argument(
        "--model_tag",
        type=str,
        help="Pretrained model tag. If specify this option, *_train_config and "
             "*_file will be overwritten",
        "*_file will be overwritten",
    )

    group = parser.add_argument_group("Beam-search related")
@@ -864,42 +744,13 @@
        help="The batch size for inference",
    )
    group.add_argument("--nbest", type=int, default=1, help="Output N-best hypotheses")
    group.add_argument("--beam_size", type=int, default=20, help="Beam size")
    group.add_argument("--penalty", type=float, default=0.0, help="Insertion penalty")
    group.add_argument(
        "--maxlenratio",
        type=float,
        default=0.0,
        help="Input length ratio to obtain max output length. "
             "If maxlenratio=0.0 (default), it uses a end-detect "
             "function "
             "to automatically find maximum hypothesis lengths."
             "If maxlenratio<0.0, its absolute value is interpreted"
             "as a constant max output length",
    )
    group.add_argument(
        "--minlenratio",
        type=float,
        default=0.0,
        help="Input length ratio to obtain min output length",
    )
    group.add_argument(
        "--ctc_weight",
        type=float,
        default=0.5,
        help="CTC weight in joint decoding",
    )
    group.add_argument("--beam_size", type=int, default=5, help="Beam size")
    group.add_argument("--lm_weight", type=float, default=1.0, help="RNNLM weight")
    group.add_argument("--ngram_weight", type=float, default=0.9, help="ngram weight")
    group.add_argument("--streaming", type=str2bool, default=False)

    group.add_argument(
        "--frontend_conf",
        default=None,
        help="",
        "--beam_search_config",
        default={},
        help="The keyword arguments for transducer beam search.",
    )
    group.add_argument("--raw_inputs", type=list, default=None)
    # example=[{'key':'EdevDEWdIYQ_0021','file':'/mnt/data/jiangyu.xzy/test_data/speech_io/SPEECHIO_ASR_ZH00007_zhibodaihuo/wav/EdevDEWdIYQ_0021.wav'}])

    group = parser.add_argument_group("Text converter related")
    group.add_argument(
@@ -908,14 +759,77 @@
        default=None,
        choices=["char", "bpe", None],
        help="The token type for ASR model. "
             "If not given, refers from the training args",
        "If not given, refers from the training args",
    )
    group.add_argument(
        "--bpemodel",
        type=str_or_none,
        default=None,
        help="The model path of sentencepiece. "
             "If not given, refers from the training args",
        "If not given, refers from the training args",
    )

    group = parser.add_argument_group("Dynamic quantization related")
    parser.add_argument(
        "--quantize_asr_model",
        type=bool,
        default=False,
        help="Apply dynamic quantization to ASR model.",
    )
    parser.add_argument(
        "--quantize_modules",
        nargs="*",
        default=None,
        help="""Module names to apply dynamic quantization on.
        The module names are provided as a list, where each name is separated
        by a comma (e.g.: --quantize-config=[Linear,LSTM,GRU]).
        Each specified name should be an attribute of 'torch.nn', e.g.:
        torch.nn.Linear, torch.nn.LSTM, torch.nn.GRU, ...""",
    )
    parser.add_argument(
        "--quantize_dtype",
        type=str,
        default="qint8",
        choices=["float16", "qint8"],
        help="Dtype for dynamic quantization.",
    )

    group = parser.add_argument_group("Streaming related")
    parser.add_argument(
        "--streaming",
        type=bool,
        default=False,
        help="Whether to perform chunk-by-chunk inference.",
    )
    parser.add_argument(
        "--simu_streaming",
        type=bool,
        default=False,
        help="Whether to simulate chunk-by-chunk inference.",
    )
    parser.add_argument(
        "--chunk_size",
        type=int,
        default=16,
        help="Number of frames in chunk AFTER subsampling.",
    )
    parser.add_argument(
        "--left_context",
        type=int,
        default=32,
        help="Number of frames in left context of the chunk AFTER subsampling.",
    )
    parser.add_argument(
        "--right_context",
        type=int,
        default=0,
        help="Number of frames in right context of the chunk AFTER subsampling.",
    )
    parser.add_argument(
        "--display_partial_hypotheses",
        type=bool,
        default=False,
        help="Whether to display partial hypotheses during chunk-by-chunk inference.",
    )

    return parser
@@ -923,24 +837,15 @@

def main(cmd=None):
    print(get_commandline_args(), file=sys.stderr)

    parser = get_parser()
    args = parser.parse_args(cmd)
    param_dict = {'hotword': args.hotword}
    kwargs = vars(args)

    kwargs.pop("config", None)
    kwargs['param_dict'] = param_dict
    inference(**kwargs)


if __name__ == "__main__":
    main()

    # from modelscope.pipelines import pipeline
    # from modelscope.utils.constant import Tasks
    #
    # inference_16k_pipline = pipeline(
    #     task=Tasks.auto_speech_recognition,
    #     model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch')
    #
    # rec_result = inference_16k_pipline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav')
    # print(rec_result)