class_docstr: Align text to audio using CTC segmentation.
  
  Usage:
      Initialize with given ASR model and parameters.
      If needed, parameters for CTC segmentation can be set with ``set_config(·)``.
      Then call the instance as function to align text within an audio file.
  
  Example:
      >>> # example file included in the ESPnet repository
      >>> import soundfile
      >>> speech, fs = soundfile.read("test_utils/ctc_align_test.wav")
      >>> # load an ASR model
      >>> from espnet_model_zoo.downloader import ModelDownloader
      >>> d = ModelDownloader()
      >>> wsjmodel = d.download_and_unpack( "kamo-naoyuki/wsj" )
      >>> # Apply CTC segmentation
      >>> aligner = CTCSegmentation( **wsjmodel )
      >>> text=["utt1 THE SALE OF THE HOTELS", "utt2 ON PROPERTY MANAGEMENT"]
      >>> aligner.set_config( gratis_blank=True )
      >>> segments = aligner( speech, text, fs=fs )
      >>> print( segments )
      utt1 utt 0.27 1.72 -0.1663 THE SALE OF THE HOTELS
      utt2 utt 4.54 6.10 -4.9646 ON PROPERTY MANAGEMENT
  
  On multiprocessing:
      To parallelize the computation with multiprocessing, these three steps
      can be separated:
      (1) ``get_lpz``: obtain the lpz,
      (2) ``prepare_segmentation_task``: prepare the task, and
      (3) ``get_segments``: perform CTC segmentation.
      Note that the function `get_segments` is a staticmethod and therefore
      independent of an already initialized CTCSegmentation object.
  
  References:
      CTC-Segmentation of Large Corpora for German End-to-end Speech Recognition
      2020, Kürzinger, Winkelbauer, Li, Watzel, Rigoll
      https://arxiv.org/abs/2007.09127
  
  More parameters are described in https://github.com/lumaku/ctc-segmentation
docstr: Determine the ratio of encoded frames to sample points.
  
  This method helps to determine the time a single encoded frame occupies.
  As the sample rate already gave the number of samples, only the ratio
  of samples per encoded CTC frame are needed. This function estimates them by
  doing one inference, which is only needed once.
  
  Args:
      speech_len: Length of randomly generated speech vector for single
          inference. Default: 215040.
  
  Returns:
      samples_to_frames_ratio: Estimated ratio.
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code: def estimate_samples_to_frames_ratio(self, speech_len=215040):
      """Determine the ratio of encoded frames to sample points.
  
      This method helps to determine the time a single encoded frame occupies.
      As the sample rate already gave the number of samples, only the ratio
      of samples per encoded CTC frame are needed. This function estimates them by
      doing one inference, which is only needed once.
  
      Args:
          speech_len: Length of randomly generated speech vector for single
              inference. Default: 215040.
  
      Returns:
          samples_to_frames_ratio: Estimated ratio.
      """
      random_input = torch.rand(speech_len)
      lpz = self.get_lpz(random_input)
      lpz_len = lpz.shape[0]
      # Most frontends (DefaultFrontend, SlidingWindow) discard trailing data
      lpz_len = lpz_len + 1
      samples_to_frames_ratio = speech_len // lpz_len
      return samples_to_frames_ratio
  
imports: import argparse
  import logging
  import sys
  from pathlib import Path
  from typing import List, Optional, TextIO, Union
  import numpy
  import soundfile
  import torch
  from ctc_segmentation import CtcSegmentationParameters, ctc_segmentation, determine_utterance_segments, prepare_text, prepare_token_list
  from typeguard import check_argument_types, check_return_type
  from espnet2.tasks.asr import ASRTask
  from espnet2.torch_utils.device_funcs import to_device
  from espnet2.utils import config_argparse
  from espnet2.utils.types import str2bool, str_or_none
  from espnet.utils.cli_utils import get_commandline_args
label_desc: Use image node_id 4 for calling the CTCSegmentation obj's underlying member method code with example usage: obj.estimate_samples_to_frames_ratio(speech_len) and returns: samples_to_frames_ratio