old_contents: from typing import Dict, List
  
  import numpy as np
  import torch
  from app.pipelines import Pipeline
  from speechbrain.pretrained import EncoderClassifier
  
  
  class AudioClassificationPipeline(Pipeline):
      def __init__(self, model_id: str):
          self.model = EncoderClassifier.from_hparams(source=model_id)
  
          self.top_k = 5
  
          # Please define a `self.sampling_rate` for this pipeline
          # to automatically read the input correctly
          self.sampling_rate = 16000
  
      def __call__(self, inputs: np.array) -> List[Dict[str, float]]:
          """
          Args:
              inputs (:obj:`np.array`):
                  The raw waveform of audio received. By default at 16KHz.
          Return:
              A :obj:`list`:. The object returned should be a list like [{"label": "text", "score": 0.9939950108528137}] containing :
                  - "label": A string representing what the label/class is. There can be multiple labels.
                  - "score": A score between 0 and 1 describing how confident the model is for this label/class.
          """
          batch = torch.from_numpy(inputs).unsqueeze(0)
          rel_length = torch.tensor([1.0])
          probs, _, _, _ = self.model.classify_batch(batch, rel_length)
          probs = torch.softmax(probs[0], dim=0)
          labels = self.model.hparams.label_encoder.decode_ndim(range(len(probs)))
          results = []
          for label, prob in sorted(zip(labels, probs), reverse=True)[: self.top_k]:
              results.append({"label": label, "score": prob.item()})
          return results
  
new_contents: from typing import Dict, List
  
  import numpy as np
  import torch
  from app.pipelines import Pipeline
  from speechbrain.pretrained import EncoderClassifier
  
  
  class AudioClassificationPipeline(Pipeline):
      def __init__(self, model_id: str):
          self.model = EncoderClassifier.from_hparams(source=model_id)
  
          self.top_k = 5
  
          # Please define a `self.sampling_rate` for this pipeline
          # to automatically read the input correctly
          self.sampling_rate = 16000
  
      def __call__(self, inputs: np.array) -> List[Dict[str, float]]:
          """
          Args:
              inputs (:obj:`np.array`):
                  The raw waveform of audio received. By default at 16KHz.
          Return:
              A :obj:`list`:. The object returned should be a list like [{"label": "text", "score": 0.9939950108528137}] containing :
                  - "label": A string representing what the label/class is. There can be multiple labels.
                  - "score": A score between 0 and 1 describing how confident the model is for this label/class.
          """
          batch = torch.from_numpy(inputs).unsqueeze(0)
          rel_length = torch.tensor([1.0])
          probs, _, _, _ = self.model.classify_batch(batch, rel_length)
          probs = torch.softmax(probs[0], dim=0)
          labels = self.model.hparams.label_encoder.decode_ndim(range(len(probs)))
          results = []
          for prob, label in sorted(zip(probs, labels), reverse=True)[: self.top_k]:
              results.append({"label": label, "score": prob.item()})
          return results
  
subject: Fix Audio Classification pipeline (#313)