class_docstr: Urmp training set.
class_docstr_tok: ["Urmp","training","set","."]
docstr: URMP dataset for either a specific instrument or all instruments.
  
  Args:
    base_dir: Base directory to URMP TFRecords.
    instrument_key: Determines which instrument to return. Choices include
      ['all', 'bn', 'cl', 'db', 'fl', 'hn', 'ob', 'sax', 'tba', 'tbn',
      'tpt', 'va', 'vc', 'vn'].
    split: Choices include ['train', 'test'].
    suffix: Choices include [None, 'batched', 'unbatched'], but broadly
      applies to any suffix adding to the file pattern.
      When suffix is not None, will add "_{suffix}" to the file pattern.
      This option is used in gs://magentadata/datasets/urmp/urmp_20210324.
      With the "batched" suffix, the dataloader will load tfrecords
      containing segmented audio samples in 4 seconds. With the "unbatched"
      suffix, the dataloader will load tfrecords containing unsegmented
      samples which could be used for learning note sequence in URMP dataset.
code: def __init__(
      self, base_dir, instrument_key="tpt", split="train", suffix=None
  ):
      """URMP dataset for either a specific instrument or all instruments.
  
      Args:
        base_dir: Base directory to URMP TFRecords.
        instrument_key: Determines which instrument to return. Choices include
          ['all', 'bn', 'cl', 'db', 'fl', 'hn', 'ob', 'sax', 'tba', 'tbn',
          'tpt', 'va', 'vc', 'vn'].
        split: Choices include ['train', 'test'].
        suffix: Choices include [None, 'batched', 'unbatched'], but broadly
          applies to any suffix adding to the file pattern.
          When suffix is not None, will add "_{suffix}" to the file pattern.
          This option is used in gs://magentadata/datasets/urmp/urmp_20210324.
          With the "batched" suffix, the dataloader will load tfrecords
          containing segmented audio samples in 4 seconds. With the "unbatched"
          suffix, the dataloader will load tfrecords containing unsegmented
          samples which could be used for learning note sequence in URMP dataset.
  
      """
      self.instrument_key = instrument_key
      self.split = split
      self.base_dir = base_dir
      self.suffix = "" if suffix is None else "_" + suffix
      super().__init__()
  
imports: import os
  from absl import logging
  from ddsp.spectral_ops import CREPE_FRAME_SIZE
  from ddsp.spectral_ops import CREPE_SAMPLE_RATE
  from ddsp.spectral_ops import get_framed_lengths
  import gin
  import tensorflow.compat.v2
  import tensorflow_datasets
label_desc: Use image node_id 1 to create a new Urmp object from inherited base classes: LegacyTFRecordProvider with example: obj = Urmp(base_dir, instrument_key, split, suffix)