code: def compute_speaker_embeddings(
      input_filepaths,
      output_file_paths,
      data_folder,
      spk_emb_encoder_path,
      spk_emb_sr,
      mel_spec_params,
      device,
  ):
      """This function processes a JSON file to compute the speaker embeddings
  
      Arguments
      ---------
      input_filepaths : list
          A list of paths to the JSON files to be processed
      output_file_paths : list
          A list of paths to the output pickle files corresponding to the input JSON files
      data_folder : str
          Path to the folder where LibriTTS data is stored
      spk_emb_encoder_path : str
          Path for the speaker encoder
      spk_emb_sr : int
          Sample rate used by the speaker embedding encoder
      mel_spec_params: dict
          Information about mel-spectrogram computation
      device : str
          Device for to be used for computation
      """
  
      # Checks if this phase is already done (if so, skips it)
      if skip(output_file_paths):
          logger.info(
              "Preparation completed in previous run, skipping."
          )
          return
  
      # Initializes the speaker encoder
      spk_emb_encoder = None
      if mel_spec_params["custom_mel_spec_encoder"]:
          # To use the custom mel-spectrogram based encoder - for compatibility with future speaker consistency loss work
          spk_emb_encoder = MelSpectrogramEncoder.from_hparams(
              source=spk_emb_encoder_path, run_opts={"device": device}
          )
      else:
          # To use the speaker encoders available with SpeechBrain
          spk_emb_encoder = EncoderClassifier.from_hparams(
              source=spk_emb_encoder_path, run_opts={"device": device}
          )
  
      # Processes data manifests files to create corresponding speaker embedding files
      for i in range(len(input_filepaths)):
          logger.info(f"Creating {output_file_paths[i]}.")
  
          speaker_embeddings = dict()  # Holds speaker embeddings
  
          json_file = open(input_filepaths[i])
          json_data = json.load(json_file)
  
          # Processes all utterances in the data manifest file
          for utt_id, utt_data in tqdm(json_data.items()):
              utt_wav_path = utt_data["wav"]
              utt_wav_path = utt_wav_path.replace(
                  "{data_root}", data_folder
              )
  
              # Loads and resamples waveforms if required
              signal, sig_sr = torchaudio.load(utt_wav_path)
              if sig_sr != spk_emb_sr:
                  signal = torchaudio.functional.resample(
                      signal, sig_sr, spk_emb_sr
                  )
              signal = signal.to(device)
  
              # Computes the speaker embedding
              if mel_spec_params["custom_mel_spec_encoder"]:
                  spk_emb = spk_emb_encoder.encode_waveform(signal)
              else:
                  spk_emb = spk_emb_encoder.encode_batch(signal)
  
              spk_emb = spk_emb.squeeze()
              spk_emb = spk_emb.detach()
  
              speaker_embeddings[utt_id] = spk_emb.cpu()
  
          # Stores the speaker embeddings at the destination
          with open(output_file_paths[i], "wb") as output_file:
              pickle.dump(
                  speaker_embeddings,
                  output_file,
                  protocol=pickle.HIGHEST_PROTOCOL,
              )
  
          logger.info(f"Created {output_file_paths[i]}.")
  
file_path: speechbrain/recipes/LibriTTS/TTS/mstacotron2/compute_speaker_embeddings.py
imports: import json
  from speechbrain.inference.encoders import MelSpectrogramEncoder
  from speechbrain.inference.classifiers import EncoderClassifier
  import torchaudio
  import pickle
  import logging
  import os
  from tqdm import tqdm
label_desc: Use image node_id 1 for calling a global function with example usage: compute_speaker_embeddings(input_filepaths, output_file_paths, data_folder, spk_emb_encoder_path, spk_emb_sr, mel_spec_params, device) without return types