def __init__(self, feature_type: FeatureType): if not (FeatureType.MFCC == feature_type): raise Exception("Only supports {}".format(FeatureType.MFCC.name)) self.data_pkl = get_dataset("savee_sr_22k_2sec_4-classes.pkl") _data, pre_train_data = randomize_split(self.data_pkl, split_ratio=0.7) rl_data, eval_data = randomize_split(_data, split_ratio=0.8) self.data = rl_data self.pre_train_data = pre_train_data self.eval_data = eval_data
def get_data(self): training_data, testing_data = randomize_split(self.data) x_train_mfcc = np.array( [d[FeatureType.MFCC.name] for d in training_data]) y_train_emo = np.array([d['y_emo'] for d in training_data]) y_train_gen = np.array([d['y_gen'] for d in training_data]) x_test_mfcc = np.array( [d[FeatureType.MFCC.name] for d in testing_data]) y_test_emo = np.array([d['y_emo'] for d in testing_data]) y_test_gen = np.array([d['y_gen'] for d in testing_data]) return (x_train_mfcc, y_train_emo, y_train_gen), (x_test_mfcc, y_test_emo, y_test_gen)
def __init__(self, feature_type: FeatureType) -> None: self.data_pkl = get_dataset( "signal-no-silent-4-class-dataset-2sec_sr_22k.pkl") for d in self.data_pkl: single_file = {} feature = librosa.feature.mfcc(d['x'], sr=SR, n_mfcc=NUM_MFCC) single_file[feature_type.name] = feature single_file['y_emo'] = d['emo'] single_file['y_gen'] = d['gen'] self.data.append(single_file) rl_data, pre_train_data = randomize_split(self.data, split_ratio=0.7) self.data = rl_data self.pre_train_data = pre_train_data
def __init__(self, sr: int = 44) -> None: self.data_pkl = get_dataset('improv-4_Class-sr_' + str(sr) + 'k_2sec.pkl') for d in self.data_pkl: self.data.append({ FeatureType.MFCC.name: d['x'], 'y_emo': d['emo'], 'y_gen': d['gen'], 'path': d['path'] }) rl_data, pre_train_data = randomize_split(self.data, split_ratio=0.7) self.data = rl_data self.pre_train_data = pre_train_data