'''
item: one piece of data
item_name: data id
wavfn: wave file path
txt: lyrics
ph: phoneme
tgfn: text grid file path (unused)
spk: dataset name
wdb: word boundary
ph_durs: phoneme durations
midi: pitch as midi notes
midi_dur: midi duration
is_slur: keep singing upon note changes
'''
from copy import deepcopy
import logging
from preprocessing.process_pipeline import File2Batch
from utils.hparams import hparams
from preprocessing.base_binarizer import BaseBinarizer
SVCSINGING_ITEM_ATTRIBUTES = ['wav_fn', 'spk_id']
class SVCBinarizer(BaseBinarizer):
def __init__(self, item_attributes=SVCSINGING_ITEM_ATTRIBUTES):
super().__init__(item_attributes)
print('spkers: ', set(item['spk_id'] for item in self.items.values()))
self.item_names = sorted(list(self.items.keys()))
self._train_item_names, self._test_item_names = self.split_train_test_set(self.item_names)
def split_train_test_set(self, item_names):
item_names = deepcopy(item_names)
if hparams['choose_test_manually']:
test_item_names = [x for x in item_names if any([x.startswith(ts) for ts in hparams['test_prefixes']])]
else:
test_item_names = item_names[-5:]
train_item_names = [x for x in item_names if x not in set(test_item_names)]
logging.info("train {}".format(len(train_item_names)))
logging.info("test {}".format(len(test_item_names)))
return train_item_names, test_item_names
@property
def train_item_names(self):
return self._train_item_names
@property
def valid_item_names(self):
return self._test_item_names
@property
def test_item_names(self):
return self._test_item_names
def load_meta_data(self):
self.items = File2Batch.file2temporary_dict()
def _phone_encoder(self):
from preprocessing.hubertinfer import Hubertencoder
return Hubertencoder(hparams['hubert_path'])
