class_docstr: Solver for MusicGen training task.
  
  Used in: https://arxiv.org/abs/2306.05284
code: def __init__(self, cfg: omegaconf.DictConfig):
      super().__init__(cfg)
      # easier access to sampling parameters
      self.generation_params = {
          "use_sampling": self.cfg.generate.lm.use_sampling,
          "temp": self.cfg.generate.lm.temp,
          "top_k": self.cfg.generate.lm.top_k,
          "top_p": self.cfg.generate.lm.top_p,
      }
      self._best_metric_name: tp.Optional[str] = "ce"
  
      self._cached_batch_writer = None
      self._cached_batch_loader = None
      if cfg.cache.path:
          if cfg.cache.write:
              self._cached_batch_writer = CachedBatchWriter(
                  Path(cfg.cache.path)
              )
              if self.cfg.cache.write_num_shards:
                  self.logger.warning(
                      "Multiple shard cache, best_metric_name will be set to None."
                  )
                  self._best_metric_name = None
          else:
              self._cached_batch_loader = CachedBatchLoader(
                  Path(cfg.cache.path),
                  cfg.dataset.batch_size,
                  cfg.dataset.num_workers,
                  min_length=self.cfg.optim.updates_per_epoch or 1,
              )
              self.dataloaders["original_train"] = self.dataloaders[
                  "train"
              ]
              self.dataloaders["train"] = self._cached_batch_loader
  
imports: from pathlib import Path
  import time
  import typing
  import warnings
  import flashy
  import math
  import omegaconf
  import torch
  from torch.nn import functional
  from .None import base, builders
  from .compression import CompressionSolver
  from ..None import metrics
  from ..None import models
  from ..data.audio_dataset import AudioDataset
  from ..data.music_dataset import MusicDataset, MusicInfo, AudioInfo
  from ..data.audio_utils import normalize_audio
  from ..modules.conditioners import JointEmbedCondition, SegmentWithAttributes, WavCondition
  from ..utils.cache import CachedBatchWriter, CachedBatchLoader
  from ..utils.samples.manager import SampleManager
  from ..utils.utils import get_dataset_from_loader, is_jsonable, warn_once, model_hash
label_desc: Use image node_id 1 to create a new MusicGenSolver object from inherited base classes: base with example: obj = MusicGenSolver(cfg)