 Prediction  ```python import torch import torch.nn as nn import torch.nn.functional as F import torchaudio from transformers import AutoConfig, Wav2Vec2FeatureExtractor from src.models import Wav2Vec2ForSpeechClassification, HubertForSpeechClassification  import librosa import IPython.display as ipd import numpy as np import pandas as pd ```  ```python device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model_name_or_path = "m3hrdadfi/hubert-large-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 = HubertForSpeechClassification.from_pretrained(model_name_or_path).to(device) ```  ```python 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 ```  ```python path = "/path/to/disgust.wav" outputs = predict(path, sampling_rate) ```  ```bash [ {'Emotion': 'anger', 'Score': '0.0%'}, {'Emotion': 'disgust', 'Score': '99.2%'}, {'Emotion': 'fear', 'Score': '0.1%'}, {'Emotion': 'happiness', 'Score': '0.3%'}, {'Emotion': 'sadness', 'Score': '0.5%'} ] ```   