class_docstr: Music dataset is an AudioDataset with music-related metadata.
  
  Args:
      info_fields_required (bool): Whether to enforce having required fields.
      merge_text_p (float): Probability of merging additional metadata to the description.
      drop_desc_p (float): Probability of dropping the original description on text merge.
      drop_other_p (float): Probability of dropping the other fields used for text augmentation.
      joint_embed_attributes (list[str]): A list of attributes for which joint embedding metadata is returned.
      paraphrase_source (str, optional): Path to the .json or .json.gz file containing the
          paraphrases for the description. The json should be a dict with keys are the
          original info path (e.g. track_path.json) and each value is a list of possible
          paraphrased.
      paraphrase_p (float): probability of taking a paraphrase.
  
  See `audiocraft.data.info_audio_dataset.InfoAudioDataset` for full initialization arguments.
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code: def __getitem__(self, index):
      wav, info = super().__getitem__(index)
      info_data = info.to_dict()
      music_info_path = Path(info.meta.path).with_suffix(".json")
  
      if Path(music_info_path).exists():
          with open(music_info_path, "r") as json_file:
              music_data = json.load(json_file)
              music_data.update(info_data)
              music_info = MusicInfo.from_dict(
                  music_data, fields_required=self.info_fields_required
              )
          if self.paraphraser is not None:
              music_info.description = self.paraphraser.sample(
                  music_info.meta.path, music_info.description
              )
          if self.merge_text_p:
              music_info = augment_music_info_description(
                  music_info,
                  self.merge_text_p,
                  self.drop_desc_p,
                  self.drop_other_p,
              )
      else:
          music_info = MusicInfo.from_dict(
              info_data, fields_required=False
          )
  
      music_info.self_wav = WavCondition(
          wav=wav[None],
          length=torch.tensor([info.n_frames]),
          sample_rate=[info.sample_rate],
          path=[info.meta.path],
          seek_time=[info.seek_time],
      )
  
      for att in self.joint_embed_attributes:
          att_value = getattr(music_info, att)
          joint_embed_cond = JointEmbedCondition(
              wav[None],
              [att_value],
              torch.tensor([info.n_frames]),
              sample_rate=[info.sample_rate],
              path=[info.meta.path],
              seek_time=[info.seek_time],
          )
          music_info.joint_embed[att] = joint_embed_cond
  
      return wav, music_info
  
imports: from dataclasses import dataclass, field, fields, replace
  import gzip
  import json
  import logging
  from pathlib import Path
  import random
  import typing
  import torch
  from .info_audio_dataset import InfoAudioDataset, AudioInfo, get_keyword_list, get_keyword, get_string
  from ..modules.conditioners import ConditioningAttributes, JointEmbedCondition, WavCondition
  from ..utils.utils import warn_once
label_desc: Use image node_id 2 for calling the MusicDataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: wav,  music_info