code: def predict_audio(audio, feature_extractor, model, label_names):
      if isinstance(audio, str):
          speech, _ = torchaudio.load(audio)
          resampler = torchaudio.transforms.Resample(
              feature_extractor.sampling_rate
          )
          speech = resampler(speech).squeeze().numpy()
      else:
          speech = audio
  
      features = feature_extractor(
          speech,
          sampling_rate=feature_extractor.sampling_rate,
          return_tensors="pt",
      )
      features["input_values"] = features["input_values"].cuda()
  
      logits = model(**features).logits
      pred_id = torch.argmax(logits, dim=-1)[0].item()
  
      return label_names[pred_id], logits.detach().cpu().numpy()
  
file_path: textlesslib/examples/expresso/classify_audio.py
imports: import torch
  import torchaudio
  from transformers import AutoFeatureExtractor, AutoModelForAudioClassification
  import os
  from tqdm import tqdm
label_desc: Use image node_id 3 for calling a global function with example usage: predict_audio(audio, feature_extractor, model, label_names) and returns: label_names,  logits