code: def __init__(
      self,
      manifest_path,
      sample_rate,
      max_sample_size=None,
      min_sample_size=0,
      shuffle=True,
      pad=False,
      normalize=False,
      num_buckets=0,
      compute_mask=False,
      text_compression_level=TextCompressionLevel.none,
      **mask_compute_kwargs,
  ):
      super().__init__(
          sample_rate=sample_rate,
          max_sample_size=max_sample_size,
          min_sample_size=min_sample_size,
          shuffle=shuffle,
          pad=pad,
          normalize=normalize,
          compute_mask=compute_mask,
          **mask_compute_kwargs,
      )
  
      self.text_compressor = TextCompressor(
          level=text_compression_level
      )
  
      skipped = 0
      self.fnames = []
      sizes = []
      self.skipped_indices = set()
  
      with open(manifest_path, "r") as f:
          self.root_dir = f.readline().strip()
          for i, line in enumerate(f):
              items = line.strip().split("\t")
              assert len(items) == 2, line
              sz = int(items[1])
              if min_sample_size is not None and sz < min_sample_size:
                  skipped += 1
                  self.skipped_indices.add(i)
                  continue
              self.fnames.append(
                  self.text_compressor.compress(items[0])
              )
              sizes.append(sz)
      logger.info(
          f"loaded {len(self.fnames)}, skipped {skipped} samples"
      )
  
      self.sizes = np.array(sizes, dtype=np.int64)
  
      try:
          import pyarrow
  
          self.fnames = pyarrow.array(self.fnames)
      except:
          logger.debug(
              "Could not create a pyarrow array. Please install pyarrow for better performance"
          )
          pass
  
      self.set_bucket_info(num_buckets)
  
imports: import logging
  import os
  import sys
  import time
  import io
  import numpy
  import torch
  import torch.nn.functional
  from ..None import FairseqDataset
  from ..data_utils import compute_block_mask_1d, get_buckets, get_bucketed_sizes
  from fairseq.data.audio.audio_utils import parse_path, read_from_stored_zip, is_sf_audio_data
  from fairseq.data.text_compressor import TextCompressor, TextCompressionLevel
label_desc: Use image node_id 1 to create a new FileAudioDataset object from inherited base classes: RawAudioDataset with example: obj = FileAudioDataset(manifest_path, sample_rate, max_sample_size, min_sample_size, shuffle, pad, normalize, num_buckets, compute_mask, text_compression_level)