class_docstr: Uses wav2vec2 to perform audio<->text alignment.
code: def align(self, audio, expected_text, audio_sample_rate=24000):
      orig_len = audio.shape[-1]
  
      with torch.no_grad():
          self.model = self.model.to(self.device)
          audio = audio.to(self.device)
          audio = torchaudio.functional.resample(
              audio, audio_sample_rate, 16000
          )
          clip_norm = (audio - audio.mean()) / torch.sqrt(
              audio.var() + 1e-7
          )
          logits = self.model(clip_norm).logits
          self.model = self.model.cpu()
  
      logits = logits[0]
      pred_string = self.tokenizer.decode(logits.argmax(-1).tolist())
  
      fixed_expectation = max_alignment(
          expected_text.lower(), pred_string
      )
      w2v_compression = orig_len // logits.shape[0]
      expected_tokens = self.tokenizer.encode(fixed_expectation)
      expected_chars = list(fixed_expectation)
      if len(expected_tokens) == 1:
          return [
              0
          ]  # The alignment is simple; there is only one token.
      expected_tokens.pop(0)  # The first token is a given.
      expected_chars.pop(0)
  
      alignments = [0]
  
      def pop_till_you_win():
          if len(expected_tokens) == 0:
              return None
          popped = expected_tokens.pop(0)
          popped_char = expected_chars.pop(0)
          while popped_char == "~":
              alignments.append(-1)
              if len(expected_tokens) == 0:
                  return None
              popped = expected_tokens.pop(0)
              popped_char = expected_chars.pop(0)
          return popped
  
      next_expected_token = pop_till_you_win()
      for i, logit in enumerate(logits):
          top = logit.argmax()
          if next_expected_token == top:
              alignments.append(i * w2v_compression)
              if len(expected_tokens) > 0:
                  next_expected_token = pop_till_you_win()
              else:
                  break
  
      pop_till_you_win()
      if not (
          len(expected_tokens) == 0
          and len(alignments) == len(expected_text)
      ):
          torch.save([audio, expected_text], "alignment_debug.pth")
          assert False, (
              "Something went wrong with the alignment algorithm. I've dumped a file, 'alignment_debug.pth' to"
              "your current working directory. Please report this along with the file so it can get fixed."
          )
  
      # Now fix up alignments. Anything with -1 should be interpolated.
      alignments.append(
          orig_len
      )  # This'll get removed but makes the algorithm below more readable.
      for i in range(len(alignments)):
          if alignments[i] == -1:
              for j in range(i + 1, len(alignments)):
                  if alignments[j] != -1:
                      next_found_token = j
                      break
              for j in range(i, next_found_token):
                  gap = alignments[next_found_token] - alignments[i - 1]
                  alignments[j] = (j - i + 1) * gap // (
                      next_found_token - i + 1
                  ) + alignments[i - 1]
  
      return alignments[:-1]
  
imports: import torch
  import torchaudio
  from transformers import Wav2Vec2CTCTokenizer, Wav2Vec2FeatureExtractor, Wav2Vec2ForCTC
label_desc: Use image node_id 2 for calling the Wav2VecAlignment obj's underlying member method code with example usage: obj.align(audio, expected_text, audio_sample_rate) and returns: alignments, list, popped, None, None