text: import os
  import re
  import glob
  from tqdm import tqdm
  import numpy as np
  import pickle
  from multiprocessing import Pool
  
  emb_dir='/mnt/data1/youngsunhere/inferred_embs/kor_dev/ckpt_000510'
  emb_list=glob.glob(emb_dir+'/*npy')
  print(f"emb_list: {len(emb_list)} embeddings")
  wav_src='/mnt/nas/01_ASR/01_korean/01_speech_text/KOR-CLN-V1'
  
  def read_emb(npy_path):
      emb=np.load(npy_path)
      uttfields=re.search(r'(?<=emb_).*(?=.npy)',os.path.basename(npy_path)).group().split('_')
      dbname="_".join(uttfields[:2])
      spkr="_".join(uttfields[2:-1])
      uttname="_".join(uttfields[2:])
  
      wavname=f"{wav_src}/{dbname}/1/{spkr}/{uttname}.wav"
      return emb,wavname
  
  pool=Pool(5)
  result=pool.map(read_emb,emb_list)
  
  print(f"done reading {len(emb_list)} embeddings")
  
  dvector_list=np.array([],dtype=float).reshape(0,256)
  label_list=[]
  
  for i in tqdm(result):
      dvector_list=np.vstack([dvector_list,i[0]])
      label_list.append(i[1])
  
  emb_dict={'dvectors':dvector_list,'labels':label_list}
  
  ckpt_epoch=emb_dir.split('/')[-1]
  with open(f'kor_dev_{ckpt_epoch}.pickle','wb') as f:
      pickle.dump(emb_dict,f)
  