def build_feature_data(self,
condition,
wav_path='./EmoDB/wav/',
laut_path='./EmoDB/lablaut/'):
assert condition in [1, 2, 3], 'unknown condition {}'.format(condition)
data_available = sorted([
s.split('.')[0] for s in os.listdir(wav_path) if s.endswith('.wav')
])
df = pd.DataFrame()
row_list = []
for ID in tqdm(data_available):
rec = Recording(ID)
if len(rec.df_tags) <= 1:
print('Excluded {} (empty tag-file)'.format(ID))
continue
if condition == 1:
row_list.append({
'ID':
ID,
'feature_vec':
rec.get_features(per_phoneme=False)
})
if condition in [2, 3]:
for i, row in rec.df_tags.iterrows():
row_list.append({
'phoneme':
row.phoneme,
'ID':
ID,
'feature_vec':
rec.get_features(row.t_start, row.t_stop)
})
pass
df = df.append(row_list)
df['sex'] = df.ID.apply(lambda x: self.speaker_info[x[:2]][0])
df['speaker_id'] = df.ID.apply(lambda x: x[:2])
df['age'] = df.ID.apply(lambda x: self.speaker_info[x[:2]][1])
df['emotion_name'] = df.ID.apply(
lambda x: self.emotion_from_ID(x, num=False))
df['emotion_label'] = df.ID.apply(lambda x: self.emotion_from_ID(x))
return df
