code: def __init__(
      self,
      split: str,
      is_train_split: bool,
      data_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,
      do_chunk=False,
      chunk_bound=-1,
      chunk_init=50,
      chunk_incr=5,
      add_eos=True,
      dedup=True,
      ref_fpu=-1,
  ):
      # It assumes texts are encoded at a fixed frame-rate
      super().__init__(
          split=split,
          is_train_split=is_train_split,
          data_cfg=data_cfg,
          audio_paths=audio_paths,
          n_frames=n_frames,
          src_texts=src_texts,
          tgt_texts=tgt_texts,
          speakers=speakers,
          src_langs=src_langs,
          tgt_langs=tgt_langs,
          ids=ids,
          tgt_dict=tgt_dict,
          pre_tokenizer=pre_tokenizer,
          bpe_tokenizer=bpe_tokenizer,
          n_frames_per_step=n_frames_per_step,
          speaker_to_id=speaker_to_id,
      )
  
      self.do_chunk = do_chunk
      self.chunk_bound = chunk_bound
      self.chunk_init = chunk_init
      self.chunk_incr = chunk_incr
      self.add_eos = add_eos
      self.dedup = dedup
      self.ref_fpu = ref_fpu
  
      self.chunk_size = -1
  
      if do_chunk:
          assert self.chunk_incr >= 0
          assert self.pre_tokenizer is None
  
file_path: fairseq/fairseq/data/audio/frm_text_to_speech_dataset.py
imports: import csv
  import logging
  import os.path
  from typing import List, Optional
  import numpy
  import torch
  from fairseq.data import Dictionary
  from fairseq.data.audio.speech_to_text_dataset import S2TDataConfig
  from fairseq.data.audio.text_to_speech_dataset import TextToSpeechDataset, TextToSpeechDatasetCreator
label_desc: Use image node_id 1 to create a new FrmTextToSpeechDataset object from inherited base classes: TextToSpeechDataset with example: obj = FrmTextToSpeechDataset(split, is_train_split, data_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, do_chunk, chunk_bound, chunk_init, chunk_incr, add_eos, dedup, ref_fpu)