Advanced Offline Emotion Recognition Models: Analyzing Emotions from Audio Files Locally

• ExHuBERT: Enhancing HuBERT Through Block Extension and Fine-Tuning on 37 Emotion Datasets

  Fine-tuned and backbone extended HuBERT Large on EmoSet++, comprising 37 datasets, totaling 150,907 samples and spanning a cumulative duration of 119.5 hours.

  Supported Languages: English, German, Chinese, French, Dutch, Greek, Italian, Spanish, Burmese, Hebrew, Swedish, Persian, Turkish, Urdu.

  import torch
  import torch.nn as nn
  from transformers import AutoModelForAudioClassification, Wav2Vec2FeatureExtractor
  
  
  # CONFIG and MODEL SETUP
  model_name = 'amiriparian/ExHuBERT'
  feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("facebook/hubert-base-ls960")
  model = AutoModelForAudioClassification.from_pretrained(model_name, trust_remote_code=True,
                                                          revision="b158d45ed8578432468f3ab8d46cbe5974380812")
  
  # Freezing half of the encoder for further transfer learning
  model.freeze_og_encoder()
  
  sampling_rate = 16000
  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
  model = model.to(device)
  
  
  # Example application from a local audiofile
  import numpy as np
  import librosa
  import torch.nn.functional as F
  # Sample taken from the Toronto emotional speech set (TESS) https://tspace.library.utoronto.ca/handle/1807/24487
  waveform, sr_wav = librosa.load("audio_002.wav")
  # Max Padding to 3 Seconds at 16k sampling rate for the best results
  waveform = feature_extractor(waveform, sampling_rate=sampling_rate,padding = 'max_length',max_length = 48000)
  waveform = waveform['input_values'][0]
  waveform = waveform.reshape(1, -1)
  waveform = torch.from_numpy(waveform).to(device)
  with torch.no_grad():
      output = model(waveform)
      output = F.softmax(output.logits, dim = 1)
      output = output.detach().cpu().numpy().round(2)
      print(output)
      
  
      # [[0.      0.      0.      1.      0.      0.]]
      #          Low          |          High                 Arousal
      # Neg.     Neut.   Pos. |  Neg.    Neut.   Pos          Valence
      # Disgust, Neutral, Kind| Anger, Surprise, Joy          Example emotions