code: def run_inference(args):
      if args.use_gpu:
          device = torch.device("cuda")
      else:
          device = torch.device("cpu")
  
      # Load the fine-tuned HuBERTPretrainModel from checkpoint.
      model = _load_checkpoint(args.checkpoint)
      model.eval().to(device)
  
      if args.use_lm:
          # get decoder files
          files = download_pretrained_files("librispeech-4-gram")
          decoder = ctc_decoder(
              lexicon=files.lexicon,
              tokens=files.tokens,
              lm=files.lm,
              nbest=args.nbest,
              beam_size=args.beam_size,
              beam_size_token=args.beam_size_token,
              beam_threshold=args.beam_threshold,
              lm_weight=args.lm_weight,
              word_score=args.word_score,
              unk_score=args.unk_score,
              sil_score=args.sil_score,
              log_add=False,
          )
      else:
          id2token = _get_id2label()
  
      dataset = torchaudio.datasets.LIBRISPEECH(
          args.librispeech_path, url=args.split
      )
  
      total_edit_distance = 0
      total_length = 0
      for idx, sample in enumerate(dataset):
          waveform, _, transcript, _, _, _ = sample
          transcript = (
              transcript.strip().lower().strip().replace("\n", "")
          )
  
          with torch.inference_mode():
              emission, _ = model(waveform.to(device))
              emission = F.log_softmax(emission, dim=-1)
          if args.use_lm:
              hypothesis = _ctc_decode(emission.cpu(), decoder)
          else:
              hypothesis = _viterbi_decode(emission, id2token)
  
          total_edit_distance += torchaudio.functional.edit_distance(
              hypothesis, transcript.split()
          )
          total_length += len(transcript.split())
  
          if idx % 100 == 0:
              logger.info(
                  f"Processed elem {idx}; WER: {total_edit_distance / total_length}"
              )
      logger.info(f"Final WER: {total_edit_distance / total_length}")
  
file_path: audio/examples/hubert/evaluate.py
imports: import argparse
  import logging
  from typing import Dict, List
  import torch
  import torch.nn.functional
  import torchaudio
  from torchaudio.models.decoder import ctc_decoder, CTCDecoder, download_pretrained_files
  from utils import _get_id2label
label_desc: Use image node_id 4 for calling a global function with example usage: run_inference(args) without return types