docstr: Compute the embedding of a collection of wavs (presumably from the same speaker) by
  averaging their embedding and L2-normalizing it.
  
  :param wavs: list of wavs a numpy arrays of float32.
  :param kwargs: extra arguments to embed_utterance()
  :return: the embedding as a numpy array of float32 of shape (model_embedding_size,).
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code: def embed_speaker(self, wavs: List[np.ndarray], **kwargs):
      """
      Compute the embedding of a collection of wavs (presumably from the same speaker) by
      averaging their embedding and L2-normalizing it.
  
      :param wavs: list of wavs a numpy arrays of float32.
      :param kwargs: extra arguments to embed_utterance()
      :return: the embedding as a numpy array of float32 of shape (model_embedding_size,).
      """
      raw_embed = np.mean(
          [
              self.embed_utterance(wav, return_partials=False, **kwargs)
              for wav in wavs
          ],
          axis=0,
      )
      return raw_embed / np.linalg.norm(raw_embed, 2)
  
file_path: TTS/TTS/vc/modules/freevc/speaker_encoder/speaker_encoder.py
imports: from pathlib import Path
  from time import perf_counter
  from typing import List, Union
  import numpy
  import torch
  from torch import nn
  from TTS.utils.io import load_fsspec
  from TTS.vc.modules.freevc.speaker_encoder import audio
  from TTS.vc.modules.freevc.speaker_encoder.hparams import *
label_desc: Use image node_id 5 for calling the SpeakerEncoder obj's underlying member method code with example usage: obj.embed_speaker(wavs) and returns: unknown