class_docstr: Urmp dataset using unsegmented data.
  
  Unsegmented here means that the data samples are not segmented to 4-second
  chunks as in UrmpMidi dataset.
class_docstr_tok: ["Urmp","dataset","using","unsegmented","data",".","Unsegmented","here","means","that","the","data","samples","are","not","segmented","to","4-second","chunks","as","in","UrmpMidi","dataset","."]
code: def features_dict(self):
      base_features = {
          "audio": tf.io.VarLenFeature(dtype=tf.float32),
          "f0_hz": tf.io.VarLenFeature(dtype=tf.float32),
          "f0_confidence": tf.io.VarLenFeature(dtype=tf.float32),
          "loudness_db": tf.io.VarLenFeature(dtype=tf.float32),
      }
      base_features.update(
          {
              "note_active_velocities": tf.io.VarLenFeature(tf.float32),
              "note_active_frame_indices": tf.io.VarLenFeature(
                  tf.float32
              ),
              "instrument_id": tf.io.FixedLenFeature([], tf.string),
              "recording_id": tf.io.FixedLenFeature([], tf.string),
              "power_db": tf.io.VarLenFeature(dtype=tf.float32),
              "note_onsets": tf.io.VarLenFeature(dtype=tf.float32),
              "note_offsets": tf.io.VarLenFeature(dtype=tf.float32),
          }
      )
      return base_features
  
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 for calling the UrmpMidiUnsegmented obj's underlying member method code with example usage: obj.features_dict() and returns: base_features