readme: ---
  language: el
  datasets:
  - aesdd
  tags:
  - audio
  - audio-classification
  - speech
  license: apache-2.0
  ---
  
  
  ~~~
  # requirement packages
  !pip install git+https://github.com/huggingface/datasets.git
  !pip install git+https://github.com/huggingface/transformers.git
  !pip install torchaudio
  !pip install librosa
  !git clone https://github.com/m3hrdadfi/soxan
  cd soxan
  ~~~
  
  
  # prediction
  ~~~
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import torchaudio
  from transformers import AutoConfig, Wav2Vec2FeatureExtractor
  
  import librosa
  import IPython.display as ipd
  import numpy as np
  import pandas as pd
  ~~~
  
  ~~~
  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
  model_name_or_path = "Bagus/wav2vec2-xlsr-greek-speech-emotion-recognition"
  config = AutoConfig.from_pretrained(model_name_or_path)
  feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name_or_path)
  sampling_rate = feature_extractor.sampling_rate
  model = Wav2Vec2ForSpeechClassification.from_pretrained(model_name_or_path).to(device)
  ~~~
  
  ~~~
  def speech_file_to_array_fn(path, sampling_rate):
      speech_array, _sampling_rate = torchaudio.load(path)
      resampler = torchaudio.transforms.Resample(_sampling_rate)
      speech = resampler(speech_array).squeeze().numpy()
      return speech
  
  
  def predict(path, sampling_rate):
      speech = speech_file_to_array_fn(path, sampling_rate)
      inputs = feature_extractor(speech, sampling_rate=sampling_rate, return_tensors="pt", padding=True)
      inputs = {key: inputs[key].to(device) for key in inputs}
  
      with torch.no_grad():
          logits = model(**inputs).logits
  
      scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0]
      outputs = [{"Emotion": config.id2label[i], "Score": f"{round(score * 100, 3):.1f}%"} for i, score in enumerate(scores)]
      return outputs
  ~~~
  
  # prediction
  ~~~
  # path for a sample
  path = '/data/jtes_v1.1/wav/f01/ang/f01_ang_01.wav'   
  outputs = predict(path, sampling_rate)
  ~~~
  
  ~~~
  [{'Emotion': 'anger', 'Score': '98.3%'},
   {'Emotion': 'disgust', 'Score': '0.0%'},
   {'Emotion': 'fear', 'Score': '0.4%'},
   {'Emotion': 'happiness', 'Score': '0.7%'},
   {'Emotion': 'sadness', 'Score': '0.5%'}]
   ~~~