code: def compute_embeddings_single(wavs, wav_lens, params):
      """Compute speaker embeddings.
  
      Arguments
      ---------
      wavs : Torch.Tensor
          Tensor containing the speech waveform (batch, time).
          Make sure the sample rate is fs=16000 Hz.
      wav_lens: Torch.Tensor
          Tensor containing the relative length for each sentence
          in the length (e.g., [0.8 0.6 1.0])
      """
      with torch.no_grad():
          feats = params["compute_features"](wavs)
          feats = params["mean_var_norm"](feats, wav_lens)
          embeddings = params["embedding_model"](feats, wav_lens)
      return embeddings.squeeze(1)
  
file_path: speechbrain/recipes/VoxCeleb/SpeakerRec/extract_speaker_embeddings.py
imports: import os
  import sys
  import numpy
  import torch
  import logging
  import torchaudio
  import speechbrain
  from hyperpyyaml import load_hyperpyyaml
  from speechbrain.utils.distributed import run_on_main
  from speechbrain.utils.data_utils import download_file
label_desc: Use image node_id 1 for calling a global function with example usage: compute_embeddings_single(wavs, wav_lens, params) and returns: embeddings