code: def __init__(
      self,
      split: str,
      is_train_split: bool,
      cfg: S2TDataConfig,
      audio_paths: List[str],
      n_frames: List[int],
      src_texts: Optional[List[str]] = None,
      tgt_texts: Optional[List[str]] = None,
      speakers: Optional[List[str]] = None,
      src_langs: Optional[List[str]] = None,
      tgt_langs: Optional[List[str]] = None,
      ids: Optional[List[str]] = None,
      tgt_dict: Optional[Dictionary] = None,
      pre_tokenizer=None,
      bpe_tokenizer=None,
      n_frames_per_step=1,
      speaker_to_id=None,
      append_eos=True,
  ):
      self.split, self.is_train_split = split, is_train_split
      self.cfg = cfg
      self.audio_paths, self.n_frames = audio_paths, n_frames
      self.n_samples = len(audio_paths)
      assert len(n_frames) == self.n_samples > 0
      assert src_texts is None or len(src_texts) == self.n_samples
      assert tgt_texts is None or len(tgt_texts) == self.n_samples
      assert speakers is None or len(speakers) == self.n_samples
      assert src_langs is None or len(src_langs) == self.n_samples
      assert tgt_langs is None or len(tgt_langs) == self.n_samples
      assert ids is None or len(ids) == self.n_samples
      assert (tgt_dict is None and tgt_texts is None) or (
          tgt_dict is not None and tgt_texts is not None
      )
      self.src_texts, self.tgt_texts = src_texts, tgt_texts
      self.src_langs, self.tgt_langs = src_langs, tgt_langs
      self.speakers = speakers
      self.tgt_dict = tgt_dict
      self.check_tgt_lang_tag()
      self.ids = ids
      self.shuffle = cfg.shuffle if is_train_split else False
  
      self.feature_transforms = (
          CompositeAudioFeatureTransform.from_config_dict(
              self.cfg.get_feature_transforms(split, is_train_split)
          )
      )
      self.waveform_transforms = (
          CompositeAudioWaveformTransform.from_config_dict(
              self.cfg.get_waveform_transforms(split, is_train_split)
          )
      )
      # TODO: add these to data_cfg.py
      self.dataset_transforms = (
          CompositeAudioDatasetTransform.from_config_dict(
              self.cfg.get_dataset_transforms(split, is_train_split)
          )
      )
  
      # check proper usage of transforms
      if self.feature_transforms and self.cfg.use_audio_input:
          logger.warning(
              "Feature transforms will not be applied. To use feature transforms, "
              "set use_audio_input as False in config."
          )
  
      self.pre_tokenizer = pre_tokenizer
      self.bpe_tokenizer = bpe_tokenizer
      self.n_frames_per_step = n_frames_per_step
      self.speaker_to_id = speaker_to_id
  
      self.tgt_lens = self.get_tgt_lens_and_check_oov()
      self.append_eos = append_eos
  
      logger.info(self.__repr__())
  
imports: import csv
  import logging
  import re
  from argparse import Namespace
  from collections import defaultdict
  from dataclasses import dataclass
  from pathlib import Path
  from typing import Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  import torch.nn.functional
  from fairseq.data import ConcatDataset, Dictionary, FairseqDataset, ResamplingDataset
  from fairseq.data import data_utils
  from fairseq.data import encoders
  from fairseq.data.audio.audio_utils import get_features_or_waveform
  from fairseq.data.audio.data_cfg import S2TDataConfig
  from fairseq.data.audio.dataset_transforms import CompositeAudioDatasetTransform
  from fairseq.data.audio.dataset_transforms.concataugment import ConcatAugment
  from fairseq.data.audio.dataset_transforms.noisyoverlapaugment import NoisyOverlapAugment
  from fairseq.data.audio.feature_transforms import CompositeAudioFeatureTransform
  from fairseq.data.audio.waveform_transforms import CompositeAudioWaveformTransform
label_desc: Use image node_id 1 to create a new SpeechToTextDataset object from inherited base classes: FairseqDataset with example: obj = SpeechToTextDataset(split, is_train_split, cfg, audio_paths, n_frames, src_texts, tgt_texts, speakers, src_langs, tgt_langs, ids, tgt_dict, pre_tokenizer, bpe_tokenizer, n_frames_per_step, speaker_to_id, append_eos)