code: def create_vocab_dict(dict_path):
      with open(dict_path, "r", encoding="utf-8") as f:
          lines = f.readlines()
          words = [line.split(" ")[0] for line in lines]
  
      num_words = len(words)
  
      vocab_dict = {
          "<s>": 0,
          "<pad>": 1,
          "</s>": 2,
          "<unk>": 3,
      }
  
      vocab_dict.update(dict(zip(words, range(4, num_words + 4))))
      return vocab_dict
  
file_path: transformers/src/transformers/models/speech_encoder_decoder/convert_speech_to_text_wav2vec2_seq2seq_original_to_pytorch.py
imports: import argparse
  import json
  import os
  import fairseq
  import torch
  from torch import nn
  from transformers import Speech2Text2Config, Speech2Text2ForCausalLM, Speech2Text2Tokenizer, SpeechEncoderDecoderConfig, SpeechEncoderDecoderModel, Wav2Vec2Config, Wav2Vec2FeatureExtractor, Wav2Vec2Model, logging
label_desc: Use image node_id 5 for calling a global function with example usage: create_vocab_dict(dict_path) and returns: vocab_dict