class_docstr: This class implements the interface for the PIQ posthoc interpreter for an audio classifier.
  
  Example
  -------
  >>> from speechbrain.inference.interpretability import PIQAudioInterpreter
  >>> tmpdir = getfixture("tmpdir")
  >>> interpreter = PIQAudioInterpreter.from_hparams(
  ...     source="speechbrain/PIQ-ESC50",
  ...     savedir=tmpdir,
  ... )
  >>> signal = torch.randn(1, 16000)
  >>> interpretation, _ = interpreter.interpret_batch(signal)
class_docstr_tok: ["This","class","implements","the","interface","for","the","PIQ","posthoc","interpreter","for","an","audio","classifier",".","Example","--","--","--","-",">",">",">","from","speechbrain.inference.interpretability","import","PIQAudioInterpreter",">",">",">","tmpdir","=","getfixture","(","``","tmpdir","''",")",">",">",">","interpreter","=","PIQAudioInterpreter.from_hparams","(","...","source=","''","speechbrain\/PIQ-ESC50","''",",","...","savedir=tmpdir",",","...",")",">",">",">","signal","=","torch.randn","(","1",",","16000",")",">",">",">","interpretation",",","_","=","interpreter.interpret_batch","(","signal",")"]
docstr: Classifies the given audiofile into the given set of labels.
  It also provides the interpretation in the audio domain.
  
  Arguments
  ---------
  path : str
      Path to audio file to classify.
  
  Returns
  -------
  x_int_sound_domain
      The interpretation in the waveform domain
  text_lab:
      The text label for the classification
  fs_model:
      The sampling frequency of the model. Useful to save the audio.
docstr_tok: ["Classifies","the","given","audiofile","into","the","given","set","of","labels",".","It","also","provides","the","interpretation","in","the","audio","domain",".","Arguments","--","--","--","--","-","path",":","str","Path","to","audio","file","to","classify",".","Returns","--","--","--","-","x_int_sound_domain","The","interpretation","in","the","waveform","domain","text_lab",":","The","text","label","for","the","classification","fs_model",":","The","sampling","frequency","of","the","model",".","Useful","to","save","the","audio","."]
code: def interpret_file(self, path, savedir="audio_cache"):
      """Classifies the given audiofile into the given set of labels.
      It also provides the interpretation in the audio domain.
  
      Arguments
      ---------
      path : str
          Path to audio file to classify.
  
      Returns
      -------
      x_int_sound_domain
          The interpretation in the waveform domain
      text_lab:
          The text label for the classification
      fs_model:
          The sampling frequency of the model. Useful to save the audio.
      """
      source, fl = split_path(path)
      path = fetch(fl, source=source, savedir=savedir)
  
      batch, fs_file = torchaudio.load(path)
      batch = batch.to(self.device)
      fs_model = self.hparams.sample_rate
  
      # resample the data if needed
      if fs_file != fs_model:
          print(
              "Resampling the audio from {} Hz to {} Hz".format(
                  fs_file, fs_model
              )
          )
          tf = torchaudio.transforms.Resample(
              orig_freq=fs_file, new_freq=fs_model
          ).to(self.device)
          batch = batch.mean(dim=0, keepdim=True)
          batch = tf(batch)
  
      x_int_sound_domain, text_lab = self.interpret_batch(batch)
      return x_int_sound_domain, text_lab, fs_model
  
file_path: speechbrain/speechbrain/inference/interpretability.py
imports: import torch
  import torchaudio
  import torch.nn.functional
  import speechbrain
  from speechbrain.utils.fetching import fetch
  from speechbrain.utils.data_utils import split_path
  from speechbrain.processing.NMF import spectral_phase
  from speechbrain.inference.interfaces import Pretrained
label_desc: Use image node_id 6 for calling the PIQAudioInterpreter obj's underlying member method code with example usage: obj.interpret_file(path, savedir) and returns: x_int_sound_domain,  text_lab,  fs_model