code: def run_asr_bleu(args):
      asr_config = retrieve_asr_config(
          args.lang, args.asr_version, json_path="./asr_model_cfgs.json"
      )
      asr_model = ASRGenerator(asr_config)
  
      eval_manifest = compose_eval_data(
          audio_dirpath=args.audio_dirpath,
          audio_format=args.audio_format,
          references_filepath=args.reference_path,
          reference_format=args.reference_format,
          reference_tsv_column=args.reference_tsv_column,
          save_manifest_filepath=None,
      )
  
      prediction_transcripts = []
      for _, eval_pair in tqdm(
          eval_manifest.iterrows(),
          desc="Transcribing predictions",
          total=len(eval_manifest),
      ):
          transcription = asr_model.transcribe_audiofile(
              eval_pair.prediction
          )
          prediction_transcripts.append(transcription.lower())
  
      if args.lang == "hok":
          prediction_transcripts = [
              merge_tailo_init_final(text)
              for text in prediction_transcripts
          ]
  
      references = eval_manifest["reference"].tolist()
      bleu_score = sacrebleu.corpus_bleu(
          prediction_transcripts, [references]
      )
  
      print(bleu_score)
  
      return prediction_transcripts, bleu_score
  
file_path: fairseq/examples/speech_to_speech/asr_bleu/compute_asr_bleu.py
imports: import os
  from typing import Dict, List
  import sacrebleu
  import pandas
  from glob import glob
  from pathlib import Path
  from utils import retrieve_asr_config, ASRGenerator
  from tqdm import tqdm
  from argparse import ArgumentParser
label_desc: Use image node_id 7 for calling a global function with example usage: run_asr_bleu(args) and returns: prediction_transcripts,  bleu_score