#!/usr/bin/env python3
import argparse
import logging
import sys
import time
import copy
import os
import codecs
import tempfile
import requests
import yaml
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

import numpy as np
import torch
import torchaudio
from typeguard import check_argument_types

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.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.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, WavFrontendOnline
from funasr.export.models.e2e_asr_paraformer import Paraformer as Paraformer_export

np.set_printoptions(threshold=np.inf)


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), ...]

    """

    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,
    ):
        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 = WavFrontendOnline(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)),
        )

        # 2. Build Language model
        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

        # 4. Build BeamSearch object
        # transducer is not supported now
        beam_search_transducer = None

        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.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()

        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:
            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

        # 6. [Optional] Build hotword list from str, local file or url

        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

    @torch.no_grad()
    def __call__(
            self, cache: dict, speech: Union[torch.Tensor], speech_lengths: Union[torch.Tensor] = None
    ):
        """Inference

        Args:
                speech: Input speech data
        Returns:
                text, token, token_int, hyp

        """
        assert check_argument_types()
        results = []
        cache_en = cache["encoder"]
        if speech.shape[1] < 16 * 60 and cache_en["is_final"]:
            if cache_en["start_idx"] == 0:
                return []
            cache_en["tail_chunk"] = True
            feats = cache_en["feats"]
            feats_len = torch.tensor([feats.shape[1]])
            self.asr_model.frontend = None
            results = self.infer(feats, feats_len, cache)
            return results
        else:
            if self.frontend is not None:
                feats, feats_len = self.frontend.forward(speech, speech_lengths, cache_en["is_final"])
                feats = to_device(feats, device=self.device)
                feats_len = feats_len.int()
                self.asr_model.frontend = None
            else:
                feats = speech
                feats_len = speech_lengths

            if feats.shape[1] != 0:
                if cache_en["is_final"]:
                    if feats.shape[1] + cache_en["chunk_size"][2] < cache_en["chunk_size"][1]:
                        cache_en["last_chunk"] = True
                    else:
                        # first chunk
                        feats_chunk1 = feats[:, :cache_en["chunk_size"][1], :]
                        feats_len = torch.tensor([feats_chunk1.shape[1]])
                        results_chunk1 = self.infer(feats_chunk1, feats_len, cache)

                        # last chunk
                        cache_en["last_chunk"] = True
                        feats_chunk2 = feats[:, -(feats.shape[1] + cache_en["chunk_size"][2] - cache_en["chunk_size"][1]):, :]
                        feats_len = torch.tensor([feats_chunk2.shape[1]])
                        results_chunk2 = self.infer(feats_chunk2, feats_len, cache)

                        return [" ".join(results_chunk1 + results_chunk2)]

                results = self.infer(feats, feats_len, cache)

        return results

    @torch.no_grad()
    def infer(self, feats: Union[torch.Tensor], feats_len: Union[torch.Tensor], cache: List = None):
        batch = {"speech": feats, "speech_lengths": feats_len}
        batch = to_device(batch, device=self.device)
        # b. Forward Encoder
        enc, enc_len = self.asr_model.encode_chunk(feats, feats_len, cache=cache)
        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_chunk(enc, cache)
        pre_acoustic_embeds, pre_token_length= predictor_outs[0], predictor_outs[1]
        if torch.max(pre_token_length) < 1:
            return []
        decoder_outs = self.asr_model.cal_decoder_with_predictor_chunk(enc, pre_acoustic_embeds, cache)
        decoder_out = decoder_outs

        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)
                token = " ".join(token)

                results.append(token)

        # assert check_return_type(results)
        return results


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,
):
    assert check_argument_types()

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

    logging.basicConfig(
        level=log_level,
        format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
    )

    export_mode = False

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

    # 1. Set random-seed
    set_all_random_seed(seed)

    # 2. Build speech2text
    speech2text_kwargs = dict(
        asr_train_config=asr_train_config,
        asr_model_file=asr_model_file,
        cmvn_file=cmvn_file,
        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,
    )

    speech2text = Speech2Text(**speech2text_kwargs)

    def _load_bytes(input):
        middle_data = np.frombuffer(input, dtype=np.int16)
        middle_data = np.asarray(middle_data)
        if middle_data.dtype.kind not in 'iu':
            raise TypeError("'middle_data' must be an array of integers")
        dtype = np.dtype('float32')
        if dtype.kind != 'f':
            raise TypeError("'dtype' must be a floating point type")

        i = np.iinfo(middle_data.dtype)
        abs_max = 2 ** (i.bits - 1)
        offset = i.min + abs_max
        array = np.frombuffer((middle_data.astype(dtype) - offset) / abs_max, dtype=np.float32)
        return array

    def _read_yaml(yaml_path: Union[str, Path]) -> Dict:
        if not Path(yaml_path).exists():
            raise FileExistsError(f'The {yaml_path} does not exist.')

        with open(str(yaml_path), 'rb') as f:
            data = yaml.load(f, Loader=yaml.Loader)
        return data

    def _prepare_cache(cache: dict = {}, chunk_size=[5,10,5], batch_size=1):
        if len(cache) > 0:
            return cache
        config = _read_yaml(asr_train_config)
        enc_output_size = config["encoder_conf"]["output_size"]
        feats_dims = config["frontend_conf"]["n_mels"] * config["frontend_conf"]["lfr_m"]
        cache_en = {"start_idx": 0, "cif_hidden": torch.zeros((batch_size, 1, enc_output_size)),
                    "cif_alphas": torch.zeros((batch_size, 1)), "chunk_size": chunk_size, "last_chunk": False,
                    "feats": torch.zeros((batch_size, chunk_size[0] + chunk_size[2], feats_dims)), "tail_chunk": False}
        cache["encoder"] = cache_en

        cache_de = {"decode_fsmn": None}
        cache["decoder"] = cache_de

        return cache

    def _cache_reset(cache: dict = {}, chunk_size=[5,10,5], batch_size=1):
        if len(cache) > 0:
            config = _read_yaml(asr_train_config)
            enc_output_size = config["encoder_conf"]["output_size"]
            feats_dims = config["frontend_conf"]["n_mels"] * config["frontend_conf"]["lfr_m"]
            cache_en = {"start_idx": 0, "cif_hidden": torch.zeros((batch_size, 1, enc_output_size)),
                        "cif_alphas": torch.zeros((batch_size, 1)), "chunk_size": chunk_size, "last_chunk": False,
                        "feats": torch.zeros((batch_size, chunk_size[0] + chunk_size[2], feats_dims)), "tail_chunk": False}
            cache["encoder"] = cache_en

            cache_de = {"decode_fsmn": None}
            cache["decoder"] = cache_de

        return cache

    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
        if data_path_and_name_and_type is not None and data_path_and_name_and_type[2] == "bytes":
            raw_inputs = _load_bytes(data_path_and_name_and_type[0])
            raw_inputs = torch.tensor(raw_inputs)
        if data_path_and_name_and_type is not None and data_path_and_name_and_type[2] == "sound":
            raw_inputs = torchaudio.load(data_path_and_name_and_type[0])[0][0]
        if data_path_and_name_and_type is None and raw_inputs is not None:
            if isinstance(raw_inputs, np.ndarray):
                raw_inputs = torch.tensor(raw_inputs)
        is_final = False
        cache = {}
        chunk_size = [5, 10, 5]
        if param_dict is not None and "cache" in param_dict:
            cache = param_dict["cache"]
        if param_dict is not None and "is_final" in param_dict:
            is_final = param_dict["is_final"]
        if param_dict is not None and "chunk_size" in param_dict:
            chunk_size = param_dict["chunk_size"]

        # 7 .Start for-loop
        # FIXME(kamo): The output format should be discussed about
        raw_inputs = torch.unsqueeze(raw_inputs, axis=0)
        asr_result_list = []
        cache = _prepare_cache(cache, chunk_size=chunk_size, batch_size=1)
        item = {}
        if data_path_and_name_and_type is not None and data_path_and_name_and_type[2] == "sound":
            sample_offset = 0
            speech_length = raw_inputs.shape[1]
            stride_size =  chunk_size[1] * 960
            cache = _prepare_cache(cache, chunk_size=chunk_size, batch_size=1)
            final_result = ""
            for sample_offset in range(0, speech_length, min(stride_size, speech_length - sample_offset)):
                if sample_offset + stride_size >= speech_length - 1:
                    stride_size = speech_length - sample_offset
                    cache["encoder"]["is_final"] = True
                else:
                    cache["encoder"]["is_final"] = False
                input_lens = torch.tensor([stride_size])
                asr_result = speech2text(cache, raw_inputs[:, sample_offset: sample_offset + stride_size], input_lens)
                if len(asr_result) != 0: 
                    final_result += " ".join(asr_result) + " "
            item = {'key': "utt", 'value': final_result.strip()}
        else:
            input_lens = torch.tensor([raw_inputs.shape[1]])
            cache["encoder"]["is_final"] = is_final
            asr_result = speech2text(cache, raw_inputs, input_lens)
            item = {'key': "utt", 'value': " ".join(asr_result)}

        asr_result_list.append(item)
        if is_final:
            cache = _cache_reset(cache, chunk_size=chunk_size, batch_size=1)
        return asr_result_list

    return _forward


def get_parser():
    parser = config_argparse.ArgumentParser(
        description="ASR 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(),
        default="INFO",
        choices=("CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"),
        help="The verbose level of logging",
    )

    parser.add_argument("--output_dir", type=str, required=True)
    parser.add_argument(
        "--ngpu",
        type=int,
        default=0,
        help="The number of gpus. 0 indicates CPU mode",
    )
    parser.add_argument("--seed", type=int, default=0, help="Random seed")
    parser.add_argument(
        "--dtype",
        default="float32",
        choices=["float16", "float32", "float64"],
        help="Data type",
    )
    parser.add_argument(
        "--num_workers",
        type=int,
        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,
        action="append",
    )
    group.add_argument("--key_file", type=str_or_none)
    group.add_argument("--allow_variable_data_keys", type=str2bool, default=False)

    group = parser.add_argument_group("The model configuration related")
    group.add_argument(
        "--asr_train_config",
        type=str,
        help="ASR training configuration",
    )
    group.add_argument(
        "--asr_model_file",
        type=str,
        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",
    )
    group.add_argument(
        "--lm_file",
        type=str,
        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",
    )

    group = parser.add_argument_group("Beam-search related")
    group.add_argument(
        "--batch_size",
        type=int,
        default=1,
        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("--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="",
    )
    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(
        "--token_type",
        type=str_or_none,
        default=None,
        choices=["char", "bpe", None],
        help="The token type for ASR model. "
             "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",
    )

    return parser


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()

