code: def load_model_dict():
      model_path_name = "/tmp/ctc_alignment_mling_uroman_model.pt"
  
      print("Downloading model and dictionary...")
      if os.path.exists(model_path_name):
          print("Model path already exists. Skipping downloading....")
      else:
          torch.hub.download_url_to_file(
              "https://dl.fbaipublicfiles.com/mms/torchaudio/ctc_alignment_mling_uroman/model.pt",
              model_path_name,
          )
          assert os.path.exists(model_path_name)
      state_dict = torch.load(model_path_name, map_location="cpu")
  
      model = wav2vec2_model(
          extractor_mode="layer_norm",
          extractor_conv_layer_config=[
              (512, 10, 5),
              (512, 3, 2),
              (512, 3, 2),
              (512, 3, 2),
              (512, 3, 2),
              (512, 2, 2),
              (512, 2, 2),
          ],
          extractor_conv_bias=True,
          encoder_embed_dim=1024,
          encoder_projection_dropout=0.0,
          encoder_pos_conv_kernel=128,
          encoder_pos_conv_groups=16,
          encoder_num_layers=24,
          encoder_num_heads=16,
          encoder_attention_dropout=0.0,
          encoder_ff_interm_features=4096,
          encoder_ff_interm_dropout=0.1,
          encoder_dropout=0.0,
          encoder_layer_norm_first=True,
          encoder_layer_drop=0.1,
          aux_num_out=31,
      )
      model.load_state_dict(state_dict)
      model.eval()
  
      dict_path_name = "/tmp/ctc_alignment_mling_uroman_model.dict"
      if os.path.exists(dict_path_name):
          print(
              "Dictionary path already exists. Skipping downloading...."
          )
      else:
          torch.hub.download_url_to_file(
              "https://dl.fbaipublicfiles.com/mms/torchaudio/ctc_alignment_mling_uroman/dictionary.txt",
              dict_path_name,
          )
          assert os.path.exists(dict_path_name)
      dictionary = {}
      with open(dict_path_name) as f:
          dictionary = {
              l.strip(): i for i, l in enumerate(f.readlines())
          }
  
      return model, dictionary
  
file_path: fairseq/examples/mms/data_prep/align_utils.py
imports: import re
  import os
  import torch
  import tempfile
  import math
  from dataclasses import dataclass
  from torchaudio.models import wav2vec2_model
label_desc: Use image node_id 5 for calling a global function with example usage: load_model_dict() and returns: model,  dictionary