class_docstr: Transcriber: speech to text pipeline
docstr: Transcribes audio to text.
  Args:
      speech: list of audio
      sampling: sampling rate
  Returns:
      list of transcribed text
docstr_tok: ["Transcribes","audio","to","text",".","Args",":","speech",":","list","of","audio","sampling",":","sampling","rate","Returns",":","list","of","transcribed","text"]
code: def _transcribe(self, speech, sampling):
      """
      Transcribes audio to text.
      Args:
          speech: list of audio
          sampling: sampling rate
      Returns:
          list of transcribed text
      """
      import torch
  
      with torch.no_grad():
          # Convert samples to features
          inputs = self.processor(
              speech,
              sampling_rate=sampling,
              padding=True,
              return_tensors="pt",
          ).input_values
          # Place inputs on tensor device
          inputs = inputs.to(self.torch_device)
          # Retrieve logits
          logits = self.model(inputs).logits
          # Decode argmax
          ids = torch.argmax(logits, dim=-1)
          return self.processor.batch_decode(ids)
  
file_path: ktrain/ktrain/text/speech/core.py
imports: from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
  from ...torch_base import TorchBase
label_desc: Use image node_id 3 for calling the Transcriber obj's underlying member method code with example usage: obj._transcribe(speech, sampling) and returns: self