#!/usr/bin/env python3
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import argparse
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import logging
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import sys
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import time
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from pathlib import Path
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from typing import Optional
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from typing import Sequence
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from typing import Tuple
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from typing import Union
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import numpy as np
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import torch
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from typeguard import check_argument_types
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from funasr.fileio.datadir_writer import DatadirWriter
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from funasr.modules.beam_search.beam_search import BeamSearchPara as BeamSearch
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from funasr.modules.beam_search.beam_search import Hypothesis
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from funasr.modules.scorers.ctc import CTCPrefixScorer
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from funasr.modules.scorers.length_bonus import LengthBonus
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from funasr.modules.subsampling import TooShortUttError
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from funasr.tasks.asr import ASRTaskParaformer as ASRTask
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from funasr.tasks.lm import LMTask
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from funasr.text.build_tokenizer import build_tokenizer
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from funasr.text.token_id_converter import TokenIDConverter
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from funasr.torch_utils.device_funcs import to_device
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from funasr.torch_utils.set_all_random_seed import set_all_random_seed
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from funasr.utils import config_argparse
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from funasr.utils.cli_utils import get_commandline_args
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from funasr.utils.types import str2bool
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from funasr.utils.types import str2triple_str
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from funasr.utils.types import str_or_none
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class Speech2Text:
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"""Speech2Text class
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Examples:
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>>> import soundfile
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>>> speech2text = Speech2Text("asr_config.yml", "asr.pth")
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>>> audio, rate = soundfile.read("speech.wav")
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>>> speech2text(audio)
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[(text, token, token_int, hypothesis object), ...]
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"""
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def __init__(
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self,
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asr_train_config: Union[Path, str] = None,
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asr_model_file: Union[Path, str] = None,
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lm_train_config: Union[Path, str] = None,
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lm_file: Union[Path, str] = None,
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token_type: str = None,
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bpemodel: str = None,
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device: str = "cpu",
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maxlenratio: float = 0.0,
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minlenratio: float = 0.0,
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dtype: str = "float32",
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beam_size: int = 20,
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ctc_weight: float = 0.5,
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lm_weight: float = 1.0,
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ngram_weight: float = 0.9,
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penalty: float = 0.0,
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nbest: int = 1,
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**kwargs,
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):
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assert check_argument_types()
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# 1. Build ASR model
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scorers = {}
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asr_model, asr_train_args = ASRTask.build_model_from_file(
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asr_train_config, asr_model_file, device
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)
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logging.info("asr_model: {}".format(asr_model))
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logging.info("asr_train_args: {}".format(asr_train_args))
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asr_model.to(dtype=getattr(torch, dtype)).eval()
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ctc = CTCPrefixScorer(ctc=asr_model.ctc, eos=asr_model.eos)
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token_list = asr_model.token_list
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scorers.update(
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ctc=ctc,
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length_bonus=LengthBonus(len(token_list)),
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)
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# 2. Build Language model
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if lm_train_config is not None:
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lm, lm_train_args = LMTask.build_model_from_file(
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lm_train_config, lm_file, device
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)
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scorers["lm"] = lm.lm
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# 3. Build ngram model
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# ngram is not supported now
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ngram = None
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scorers["ngram"] = ngram
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# 4. Build BeamSearch object
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# transducer is not supported now
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beam_search_transducer = None
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weights = dict(
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decoder=1.0 - ctc_weight,
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ctc=ctc_weight,
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lm=lm_weight,
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ngram=ngram_weight,
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length_bonus=penalty,
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)
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beam_search = BeamSearch(
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beam_size=beam_size,
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weights=weights,
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scorers=scorers,
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sos=asr_model.sos,
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eos=asr_model.eos,
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vocab_size=len(token_list),
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token_list=token_list,
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pre_beam_score_key=None if ctc_weight == 1.0 else "full",
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)
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beam_search.to(device=device, dtype=getattr(torch, dtype)).eval()
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for scorer in scorers.values():
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if isinstance(scorer, torch.nn.Module):
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scorer.to(device=device, dtype=getattr(torch, dtype)).eval()
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logging.info(f"Beam_search: {beam_search}")
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logging.info(f"Decoding device={device}, dtype={dtype}")
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# 5. [Optional] Build Text converter: e.g. bpe-sym -> Text
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if token_type is None:
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token_type = asr_train_args.token_type
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if bpemodel is None:
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bpemodel = asr_train_args.bpemodel
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if token_type is None:
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tokenizer = None
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elif token_type == "bpe":
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if bpemodel is not None:
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tokenizer = build_tokenizer(token_type=token_type, bpemodel=bpemodel)
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else:
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tokenizer = None
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else:
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tokenizer = build_tokenizer(token_type=token_type)
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converter = TokenIDConverter(token_list=token_list)
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logging.info(f"Text tokenizer: {tokenizer}")
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self.asr_model = asr_model
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self.asr_train_args = asr_train_args
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self.converter = converter
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self.tokenizer = tokenizer
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self.beam_search = beam_search
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self.beam_search_transducer = beam_search_transducer
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self.maxlenratio = maxlenratio
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self.minlenratio = minlenratio
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self.device = device
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self.dtype = dtype
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self.nbest = nbest
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@torch.no_grad()
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def __call__(
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self, speech: Union[torch.Tensor, np.ndarray]
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):
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"""Inference
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Args:
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data: Input speech data
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Returns:
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text, token, token_int, hyp
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"""
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assert check_argument_types()
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# Input as audio signal
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if isinstance(speech, np.ndarray):
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speech = torch.tensor(speech)
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# data: (Nsamples,) -> (1, Nsamples)
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speech = speech.unsqueeze(0).to(getattr(torch, self.dtype))
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lfr_factor = max(1, (speech.size()[-1]//80)-1)
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# lengths: (1,)
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lengths = speech.new_full([1], dtype=torch.long, fill_value=speech.size(1))
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batch = {"speech": speech, "speech_lengths": lengths}
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# a. To device
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batch = to_device(batch, device=self.device)
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# b. Forward Encoder
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enc, enc_len = self.asr_model.encode(**batch)
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if isinstance(enc, tuple):
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enc = enc[0]
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assert len(enc) == 1, len(enc)
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predictor_outs = self.asr_model.calc_predictor(enc, enc_len)
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pre_acoustic_embeds, pre_token_length = predictor_outs[0], predictor_outs[1]
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pre_token_length = torch.tensor([pre_acoustic_embeds.size(1)], device=pre_acoustic_embeds.device)
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decoder_outs = self.asr_model.cal_decoder_with_predictor(enc, enc_len, pre_acoustic_embeds, pre_token_length)
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decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
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nbest_hyps = self.beam_search(
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x=enc[0], am_scores=decoder_out[0], maxlenratio=self.maxlenratio, minlenratio=self.minlenratio
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)
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nbest_hyps = nbest_hyps[: self.nbest]
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results = []
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for hyp in nbest_hyps:
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assert isinstance(hyp, (Hypothesis)), type(hyp)
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# remove sos/eos and get results
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last_pos = -1
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if isinstance(hyp.yseq, list):
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token_int = hyp.yseq[1:last_pos]
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else:
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token_int = hyp.yseq[1:last_pos].tolist()
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# remove blank symbol id, which is assumed to be 0
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token_int = list(filter(lambda x: x != 0, token_int))
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# Change integer-ids to tokens
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token = self.converter.ids2tokens(token_int)
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if self.tokenizer is not None:
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text = self.tokenizer.tokens2text(token)
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else:
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text = None
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results.append((text, token, token_int, hyp, speech.size(1), lfr_factor))
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# assert check_return_type(results)
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return results
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def inference(
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output_dir: str,
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maxlenratio: float,
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minlenratio: float,
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batch_size: int,
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dtype: str,
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beam_size: int,
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ngpu: int,
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seed: int,
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ctc_weight: float,
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lm_weight: float,
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ngram_weight: float,
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penalty: float,
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nbest: int,
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num_workers: int,
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log_level: Union[int, str],
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data_path_and_name_and_type: Sequence[Tuple[str, str, str]],
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key_file: Optional[str],
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asr_train_config: Optional[str],
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asr_model_file: Optional[str],
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lm_train_config: Optional[str],
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lm_file: Optional[str],
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word_lm_train_config: Optional[str],
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token_type: Optional[str],
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bpemodel: Optional[str],
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allow_variable_data_keys: bool,
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**kwargs,
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):
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assert check_argument_types()
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if batch_size > 1:
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raise NotImplementedError("batch decoding is not implemented")
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if word_lm_train_config is not None:
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raise NotImplementedError("Word LM is not implemented")
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if ngpu > 1:
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raise NotImplementedError("only single GPU decoding is supported")
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logging.basicConfig(
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level=log_level,
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format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
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)
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if ngpu >= 1:
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device = "cuda"
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else:
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device = "cpu"
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# 1. Set random-seed
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set_all_random_seed(seed)
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# 2. Build speech2text
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speech2text_kwargs = dict(
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asr_train_config=asr_train_config,
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asr_model_file=asr_model_file,
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lm_train_config=lm_train_config,
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lm_file=lm_file,
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token_type=token_type,
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bpemodel=bpemodel,
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device=device,
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maxlenratio=maxlenratio,
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minlenratio=minlenratio,
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dtype=dtype,
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beam_size=beam_size,
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ctc_weight=ctc_weight,
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lm_weight=lm_weight,
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ngram_weight=ngram_weight,
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penalty=penalty,
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nbest=nbest,
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)
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speech2text = Speech2Text(**speech2text_kwargs)
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# 3. Build data-iterator
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loader = ASRTask.build_streaming_iterator(
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data_path_and_name_and_type,
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dtype=dtype,
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batch_size=batch_size,
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key_file=key_file,
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num_workers=num_workers,
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preprocess_fn=ASRTask.build_preprocess_fn(speech2text.asr_train_args, False),
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collate_fn=ASRTask.build_collate_fn(speech2text.asr_train_args, False),
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allow_variable_data_keys=allow_variable_data_keys,
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inference=True,
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)
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forward_time_total = 0.0
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length_total = 0.0
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# 7 .Start for-loop
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# FIXME(kamo): The output format should be discussed about
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with DatadirWriter(output_dir) as writer:
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for keys, batch in loader:
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assert isinstance(batch, dict), type(batch)
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assert all(isinstance(s, str) for s in keys), keys
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_bs = len(next(iter(batch.values())))
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assert len(keys) == _bs, f"{len(keys)} != {_bs}"
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batch = {k: v[0] for k, v in batch.items() if not k.endswith("_lengths")}
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logging.info("decoding, utt_id: {}".format(keys))
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# N-best list of (text, token, token_int, hyp_object)
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try:
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time_beg = time.time()
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results = speech2text(**batch)
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time_end = time.time()
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forward_time = time_end - time_beg
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lfr_factor = results[0][-1]
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length = results[0][-2]
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results = [results[0][:-2]]
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forward_time_total += forward_time
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length_total += length
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logging.info(
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"decoding, feature length: {}, forward_time: {:.4f}, rtf: {:.4f}".
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format(length, forward_time, 100 * forward_time / (length*lfr_factor)))
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except TooShortUttError as e:
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logging.warning(f"Utterance {keys} {e}")
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hyp = Hypothesis(score=0.0, scores={}, states={}, yseq=[])
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results = [[" ", ["<space>"], [2], hyp]] * nbest
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# Only supporting batch_size==1
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key = keys[0]
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for n, (text, token, token_int, hyp) in zip(range(1, nbest + 1), results):
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# Create a directory: outdir/{n}best_recog
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ibest_writer = writer[f"{n}best_recog"]
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# Write the result to each file
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ibest_writer["token"][key] = " ".join(token)
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ibest_writer["token_int"][key] = " ".join(map(str, token_int))
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ibest_writer["score"][key] = str(hyp.score)
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if text is not None:
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ibest_writer["text"][key] = text
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logging.info("decoding, predictions: {}".format(text))
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logging.info("decoding, feature length total: {}, forward_time total: {:.4f}, rtf avg: {:.4f}".
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format(length_total, forward_time_total, 100 * forward_time_total / (length_total*lfr_factor)))
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def get_parser():
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parser = config_argparse.ArgumentParser(
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description="ASR Decoding",
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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)
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# Note(kamo): Use '_' instead of '-' as separator.
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# '-' is confusing if written in yaml.
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parser.add_argument(
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"--log_level",
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type=lambda x: x.upper(),
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default="INFO",
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choices=("CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"),
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help="The verbose level of logging",
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)
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parser.add_argument("--output_dir", type=str, required=True)
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parser.add_argument(
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"--ngpu",
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type=int,
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default=0,
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help="The number of gpus. 0 indicates CPU mode",
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)
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parser.add_argument("--seed", type=int, default=0, help="Random seed")
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parser.add_argument(
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"--dtype",
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default="float32",
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choices=["float16", "float32", "float64"],
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help="Data type",
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)
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parser.add_argument(
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"--num_workers",
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type=int,
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default=1,
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help="The number of workers used for DataLoader",
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)
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group = parser.add_argument_group("Input data related")
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group.add_argument(
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"--data_path_and_name_and_type",
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type=str2triple_str,
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required=True,
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action="append",
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)
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group.add_argument("--key_file", type=str_or_none)
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group.add_argument("--allow_variable_data_keys", type=str2bool, default=False)
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group = parser.add_argument_group("The model configuration related")
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group.add_argument(
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"--asr_train_config",
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type=str,
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help="ASR training configuration",
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)
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group.add_argument(
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"--asr_model_file",
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type=str,
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help="ASR model parameter file",
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)
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group.add_argument(
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"--lm_train_config",
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type=str,
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help="LM training configuration",
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)
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group.add_argument(
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"--lm_file",
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type=str,
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help="LM parameter file",
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)
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group.add_argument(
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"--word_lm_train_config",
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type=str,
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help="Word LM training configuration",
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)
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group.add_argument(
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"--word_lm_file",
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type=str,
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help="Word LM parameter file",
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)
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group.add_argument(
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"--ngram_file",
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type=str,
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help="N-gram parameter file",
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)
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group.add_argument(
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"--model_tag",
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type=str,
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help="Pretrained model tag. If specify this option, *_train_config and "
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"*_file will be overwritten",
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)
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group = parser.add_argument_group("Beam-search related")
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group.add_argument(
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"--batch_size",
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type=int,
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default=1,
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help="The batch size for inference",
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)
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group.add_argument("--nbest", type=int, default=1, help="Output N-best hypotheses")
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group.add_argument("--beam_size", type=int, default=20, help="Beam size")
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group.add_argument("--penalty", type=float, default=0.0, help="Insertion penalty")
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group.add_argument(
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"--maxlenratio",
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type=float,
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default=0.0,
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help="Input length ratio to obtain max output length. "
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"If maxlenratio=0.0 (default), it uses a end-detect "
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"function "
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"to automatically find maximum hypothesis lengths."
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"If maxlenratio<0.0, its absolute value is interpreted"
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"as a constant max output length",
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)
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group.add_argument(
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"--minlenratio",
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type=float,
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default=0.0,
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help="Input length ratio to obtain min output length",
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)
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group.add_argument(
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"--ctc_weight",
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type=float,
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default=0.5,
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help="CTC weight in joint decoding",
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)
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group.add_argument("--lm_weight", type=float, default=1.0, help="RNNLM weight")
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group.add_argument("--ngram_weight", type=float, default=0.9, help="ngram weight")
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group.add_argument("--streaming", type=str2bool, default=False)
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group.add_argument(
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"--frontend_conf",
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default=None,
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help="",
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)
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group = parser.add_argument_group("Text converter related")
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group.add_argument(
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"--token_type",
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type=str_or_none,
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default=None,
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choices=["char", "bpe", None],
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help="The token type for ASR model. "
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"If not given, refers from the training args",
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)
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group.add_argument(
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"--bpemodel",
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type=str_or_none,
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default=None,
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help="The model path of sentencepiece. "
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"If not given, refers from the training args",
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)
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return parser
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|
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def main(cmd=None):
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print(get_commandline_args(), file=sys.stderr)
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parser = get_parser()
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args = parser.parse_args(cmd)
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kwargs = vars(args)
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kwargs.pop("config", None)
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inference(**kwargs)
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if __name__ == "__main__":
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main()
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