code: def main(args):
      _init_logger(args.debug)
  
      if not args.exp_dir.exists():
          args.exp_dir.mkdir()
      if args.feat_type == "mfcc":
          data_dir = args.exp_dir / "data" / "mfcc"
      else:
          data_dir = (
              args.exp_dir
              / "data"
              / f"{args.feat_type}_{args.layer_index}"
          )
      data_dir.mkdir(parents=True, exist_ok=True)
  
      tsv_dir = data_dir / "tsv"
      feat_dir = data_dir / "feat"
      km_dir = data_dir / "km_model"
      label_dir = data_dir / "label"
  
      if args.use_gpu:
          device = torch.device("cuda")
      else:
          device = torch.device("cpu")
  
      # Create file lists for training and validation (optional)
      create_tsv(args.root_dir, tsv_dir)
  
      # Extract features for KMeans clustering
      if not feat_dir.exists():
          feat_dir.mkdir()
  
      for split in ["train", "valid"]:
          for rank in range(1, args.num_rank + 1):
              dump_features(
                  tsv_dir / f"{args.dataset}_{split}.tsv",
                  feat_dir,
                  split,
                  rank,
                  args.num_rank,
                  device,
                  args.feat_type,
                  args.layer_index,
                  args.checkpoint_path,
                  16_000,
              )
  
      # Fit KMeans clustering model
      learn_kmeans(
          feat_dir,
          "train",
          args.num_rank,
          km_dir,
          args.num_cluster,
          args.percent,
      )
  
      # Predict labels for MFCC or HuBERT features
      for split in ["train", "valid"]:
          get_km_label(
              feat_dir,
              km_dir,
              label_dir,
              split,
              args.num_rank,
              device,
          )
  
file_path: audio/examples/hubert/preprocess.py
imports: import logging
  from argparse import ArgumentParser, RawTextHelpFormatter
  from pathlib import Path
  import torch
  from utils import create_tsv, dump_features, get_km_label, learn_kmeans
label_desc: Use image node_id 3 for calling a global function with example usage: main(args) without return types