class_docstr: Uses wav2vec2 to perform audio<->text alignment.
code: def redact(self, audio, expected_text, audio_sample_rate=24000):
      if "[" not in expected_text:
          return audio
      splitted = expected_text.split("[")
      fully_split = [splitted[0]]
      for spl in splitted[1:]:
          assert (
              "]" in spl
          ), 'Every "[" character must be paired with a "]" with no nesting.'
          fully_split.extend(spl.split("]"))
  
      # At this point, fully_split is a list of strings, with every other string being something that should be redacted.
      non_redacted_intervals = []
      last_point = 0
      for i in range(len(fully_split)):
          if i % 2 == 0:
              end_interval = max(
                  0, last_point + len(fully_split[i]) - 1
              )
              non_redacted_intervals.append((last_point, end_interval))
          last_point += len(fully_split[i])
  
      bare_text = "".join(fully_split)
      alignments = self.align(audio, bare_text, audio_sample_rate)
  
      output_audio = []
      for nri in non_redacted_intervals:
          start, stop = nri
          output_audio.append(
              audio[:, alignments[start] : alignments[stop]]
          )
      return torch.cat(output_audio, dim=-1)
  
imports: import torch
  import torchaudio
  from transformers import Wav2Vec2CTCTokenizer, Wav2Vec2FeatureExtractor, Wav2Vec2ForCTC
label_desc: Use image node_id 3 for calling the Wav2VecAlignment obj's underlying member method code with example usage: obj.redact(audio, expected_text, audio_sample_rate) and returns: torch, audio