code: def test_speaker_embeddings(self):
      processor = BarkProcessor.from_pretrained(
          pretrained_processor_name_or_path=self.checkpoint,
          speaker_embeddings_dict_path=self.speaker_embeddings_dict_path,
      )
  
      seq_len = 35
      nb_codebooks_coarse = 2
      nb_codebooks_total = 8
  
      voice_preset = {
          "semantic_prompt": np.ones(seq_len),
          "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len)),
          "fine_prompt": np.ones((nb_codebooks_total, seq_len)),
      }
  
      # test providing already loaded voice_preset
      inputs = processor(
          text=self.input_string, voice_preset=voice_preset
      )
  
      processed_voice_preset = inputs["history_prompt"]
      for key in voice_preset:
          self.assertListEqual(
              voice_preset[key].tolist(),
              processed_voice_preset.get(key, np.array([])).tolist(),
          )
  
      # test loading voice preset from npz file
      tmpfilename = os.path.join(self.tmpdirname, "file.npz")
      np.savez(tmpfilename, **voice_preset)
      inputs = processor(
          text=self.input_string, voice_preset=tmpfilename
      )
      processed_voice_preset = inputs["history_prompt"]
  
      for key in voice_preset:
          self.assertListEqual(
              voice_preset[key].tolist(),
              processed_voice_preset.get(key, np.array([])).tolist(),
          )
  
      # test loading voice preset from the hub
      inputs = processor(
          text=self.input_string, voice_preset=self.voice_preset
      )
  
file_path: transformers/tests/models/bark/test_processor_bark.py
imports: import os
  import shutil
  import tempfile
  import unittest
  import numpy
  from transformers import AutoTokenizer, BarkProcessor
  from transformers.testing_utils import require_torch, slow
label_desc: Use image node_id 6 for calling the BarkProcessorTest obj's underlying member method code with example usage: obj.test_speaker_embeddings() without return types