readme: ---
  inference: true
  pipeline_tag: audio-classification
  tags:
  - speech
  - audio
  - HUBert
  ---
  
  
  Working example of using pretrained model to predict emotion in local audio file
  
  ```
  
  def predict_emotion_hubert(audio_file):
      """ inspired by an example from https://github.com/m3hrdadfi/soxan """
      from audio_models import HubertForSpeechClassification
      from transformers import  Wav2Vec2FeatureExtractor, AutoConfig
      import torch.nn.functional as F
      import torch
      import numpy as np
      from pydub import AudioSegment
  
      model = HubertForSpeechClassification.from_pretrained("Rajaram1996/Hubert_emotion") # Downloading: 362M
      feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("facebook/hubert-base-ls960")
      sampling_rate=16000 # defined by the model; must convert mp3 to this rate.
      config = AutoConfig.from_pretrained("Rajaram1996/Hubert_emotion")
  
      def speech_file_to_array(path, sampling_rate):
          # using torchaudio...
          # speech_array, _sampling_rate = torchaudio.load(path)
          # resampler = torchaudio.transforms.Resample(_sampling_rate, sampling_rate)
          # speech = resampler(speech_array).squeeze().numpy()
          sound = AudioSegment.from_file(path)
          sound = sound.set_frame_rate(sampling_rate)
          sound_array = np.array(sound.get_array_of_samples())
          return sound_array
  
      sound_array = speech_file_to_array(audio_file, sampling_rate)
      inputs = feature_extractor(sound_array, sampling_rate=sampling_rate, return_tensors="pt", padding=True)
      inputs = {key: inputs[key].to("cpu").float() for key in inputs}
  
      with torch.no_grad():
          logits = model(**inputs).logits
  
      scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0]
      outputs = [{
          "emo": config.id2label[i],
          "score": round(score * 100, 1)}
          for i, score in enumerate(scores)
      ]
      return [row for row in sorted(outputs, key=lambda x:x["score"], reverse=True) if row['score'] != '0.0%'][:2]
  ```
  
  ```
  
  result = predict_emotion_hubert("male-crying.mp3")
  >>> result
  [{'emo': 'male_sad', 'score': 91.0}, {'emo': 'male_fear', 'score': 4.8}]
  ```
  
  