| import librosa | |
| import torch | |
| import pandas as pd | |
| from torch.utils.data import Dataset | |
| class EmotionDataset(Dataset): | |
| def __init__(self, csv_file, processor): | |
| self.data = pd.read_csv(csv_file) | |
| self.processor = processor | |
| self.emotion_labels = {"joie": 0, "colère": 1, "neutre": 2} | |
| def __len__(self): | |
| return len(self.data) | |
| def __getitem__(self, idx): | |
| audio_path = self.data.iloc[idx, 0] | |
| label = self.emotion_labels[self.data.iloc[idx, 1]] | |
| waveform, _ = librosa.load(audio_path, sr=16000) | |
| input_values = self.processor(waveform, return_tensors="pt", sampling_rate=16000).input_values | |
| return input_values.squeeze(0), torch.tensor(label, dtype=torch.long) | |