code: def test_CTCSegmentation(asr_model: EncoderDecoderASR):
      """Test CTC segmentation.
  
      Instead of pre-loading an ASR model and inferring an audio file, it is also
      possible to use randomly generated ASR models and speech data. Please note
      that with random data, there will be a small chance that this test might
      randomly fail.
      """
  
      import numpy as np
      from speechbrain.alignment.ctc_segmentation import CTCSegmentation
      from speechbrain.alignment.ctc_segmentation import (
          CTCSegmentationTask,
      )
  
      # speech either from the test audio file or random
      # example file included in the speechbrain repository
      # speech = "./samples/audio_samples/example1.wav"
      num_samples = 100000
      speech = np.random.randn(num_samples)
  
      # text includes:
      #   one blank line
      #   kaldi-style utterance names
      #   one char not included in char list
      text = (
          "\n"
          "utt_a THE BIRCH CANOE\n"
          "utt_b SLID ON THE\n"
          "utt_c SMOOTH PLANKS\n"
      )
      aligner = CTCSegmentation(
          asr_model=asr_model,
          kaldi_style_text=True,
          min_window_size=10,
      )
      segments = aligner(speech, text)
      # check segments
      assert isinstance(segments, CTCSegmentationTask)
      kaldi_text = str(segments)
      first_line = kaldi_text.splitlines()[0]
      assert "utt_a" == first_line.split(" ")[0]
      start, end, score = segments.segments[0]
      assert start > 0.0
      assert end >= start
      assert score < 0.0
      # check options and align with "classic" text converter
      option_dict = {
          "time_stamps": "fixed",
          "samples_to_frames_ratio": 512,
          "min_window_size": 100,
          "max_window_size": 20000,
          "set_blank": 0,
          "scoring_length": 10,
          "replace_spaces_with_blanks": True,
          "gratis_blank": True,
          "kaldi_style_text": False,
          "text_converter": "classic",
      }
      aligner.set_config(**option_dict)
      assert aligner.warned_about_misconfiguration
      text = [
          "THE LITTLE GIRL",
          "HAD BEEN ASLEEP",
          "BUT SHE HEARD THE RAPS",
          "AND OPENED THE DOOR",
      ]
      segments = aligner(speech, text, name="foo")
      segments_str = str(segments)
      first_line = segments_str.splitlines()[0]
      assert "foo_0000" == first_line.split(" ")[0]
      # test the ratio estimation (result: 509)
      ratio = aligner.estimate_samples_to_frames_ratio()
      assert 400 <= ratio <= 700
  
imports: from speechbrain.inference.ASR import EncoderDecoderASR
  import pytest
label_desc: Use image node_id 2 for calling a global function with example usage: test_CTCSegmentation(asr_model) without return types