Eydr/

Keyboradsoundai

def __getitem__(self, index):
    audio_path = self.data['audio'][index]
    label = self.data['label'][index]

    # Read the audio file and convert it to a spectrogram
    audio, _ = librosa.load(audio_path)
    spectrogram = librosa.feature.melspectrogram(audio, n_mels=128)

    # Pad the spectrogram to have the same length for all samples
    spectrogram = torch.tensor(spectrogram).unsqueeze(0).expand(-1, self.max_len - spectrogram.shape[1])

    # Convert the label to a tensor
    label = torch.tensor(label)

    # Return the spectrogram and label as a dataset tuple
    return spectrogram, label

def __len__(self):
    return len(self.data['audio'])