code: def __init__(self, model_name, input_wavs_sr, cuda):
      self.cuda = cuda
      self.config = AutoConfig.from_pretrained(model_name)
      self.model = AutoModelForCTC.from_pretrained(model_name)
      self.model.eval()
      if self.cuda:
          self.model.to(device="cuda")
      self.processor = AutoProcessor.from_pretrained(model_name)
      self.resampler = torchaudio.transforms.Resample(
          input_wavs_sr, 16_000
      )
      blank_id = 0
      vocab = list(self.processor.tokenizer.get_vocab().keys())
      for i in range(len(vocab)):
          if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
              blank_id = i
      print("Blank Token id [PAD]/<pad>", blank_id)
      self.blank_id = blank_id
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/examples/research_projects/wav2vec2/alignment.py
imports: import argparse
  import os
  from dataclasses import dataclass
  import torch
  import torchaudio
  from tqdm import tqdm
  from transformers import AutoConfig, AutoModelForCTC, AutoProcessor
label_desc: Use image node_id 1 to create a new Wav2Vec2Aligner object  with example: obj = Wav2Vec2Aligner(model_name, input_wavs_sr, cuda)