code: def forward(self, x):
      if len(x.shape) == 1:
          x = x.unsqueeze(0)
  
      fbank = compute_log_fbank(
          x
      )  # x: waveform tensors with (B, T) shape
      norm_fbank = normalize(fbank)
      embedding = self.model(norm_fbank.transpose([0, 2, 1])).transpose(
          [0, 2, 1]
      )
      norm_embedding = normalize(
          x=embedding,
          global_mean=self.global_emb_mean,
          global_std=self.global_emb_std,
      )
  
      return norm_embedding
  
file_path: PaddleHub/modules/audio/speaker_recognition/ecapa_tdnn_voxceleb/module.py
imports: import os
  import re
  from typing import List
  from typing import Union
  import numpy
  import paddle
  import paddleaudio
  from .ecapa_tdnn import ECAPA_TDNN
  from .feature import compute_log_fbank
  from .feature import normalize
  from paddlehub.module.module import moduleinfo
  from paddlehub.utils.log import logger
label_desc: Use image node_id 5 for calling the SpeakerRecognition obj's underlying member method code with example usage: obj.forward(x) and returns: norm_embedding