def __getitem__(self, batch_index):
''' get batch_index's batch data '''
assert self._file_suffix == '.npy'
logging.debug(f'''
batch_index: {batch_index},
num_batches: {len(self)},
num exapmples: {self.num_examples},
label dict: {self.classes}''')
indexes = self.indexes[batch_index *
self.batch_size:(batch_index + 1) *
self.batch_size]
#logging.info(f"examples meta: {self.examples_meta}")
batch_meta = [self.examples_meta[i] for i in indexes]
if self.shuffle:
random.shuffle(batch_meta)
logging.debug(f"batch metah: {batch_meta}")
feats = []
labels = []
filenames = []
for _, (filename, label, seg) in enumerate(batch_meta):
feat = np.load(filename)
# shape : [nframe, feat_size, 3]
feat = feat_lib.add_delta_delta(feat, self._feature_size, order=2)
if self.feature_shape[-1] == 1:
feat = feat[:, :, 0:1]
seg = list(map(self.sample_to_frame, seg))
if seg[2]:
# need padding
feat = np.pad(feat, [(0, seg[2]), (0, 0), (0, 0)],
mode='constant')
feat = feat[seg[0]:seg[1], :, :]
assert len(feat) == self.sample_to_frame(
self.example_len), "{} {} {} {} {} {}".format(
filename, seg, len(feat), self.example_len,
self.sample_to_frame(self.example_len), seg[2])
# convert string label to int label
labelid = self.class_id(label)
feats.append(feat)
filenames.append(filename)
labels.append(labelid)
features = {
'inputs': np.array(feats, dtype=np.float64),
'labels': np.array(labels, dtype=np.int32),
}
one_hot_label = np.array(labels, dtype=np.int32)
one_hot_label = tf.keras.utils.to_categorical(one_hot_label,
num_classes=len(
self.classes))
return features, one_hot_label
def generate_data(self):
''' generate one example'''
use_text = self.taskconf['text']['enable']
# total files
total = len(self._train_by_filename.values())
self._epoch += 1 # epcoh from 1
batch = []
np.random.shuffle(self.data_items)
for i, (filename, examples) in enumerate(self.data_items):
#logging.info("example info", filename, examples)
# convert txt to ids
if use_text:
text = _load_text('.'.join(filename.split('.')[:-1]))
text2id = self._word_table_lookup(text)
else:
text2id = np.array([0] * self._max_text_len)
# gen audio or load feat
if self._file_suffix == '.wav':
sr, raw_samples = feat_lib.load_wav(filename) #pylint: disable=invalid-name
for label, seg, clip_id in examples:
# examples of one file
samples = raw_samples
if seg[2]:
samples = np.pad(samples, [0, seg[2]], mode='constant')
samples = samples[seg[0]:seg[1]]
assert len(samples) == self.example_len, "{} {}".format(
filename, seg)
labelid = self.class_id(label)
if self.use_distilling:
soft_label = self.teacher(feat)
else:
class_num = self.taskconf['classes']['num']
soft_label = [0] * class_num
if use_text:
if clip_id == 0:
# only add into batch when meet the first clip
batch.append((samples, text2id, labelid, filename,
clip_id, soft_label))
else:
batch.append((samples, text2id, labelid, filename,
clip_id, soft_label))
else:
feat = np.load(filename)
# shape : [nframe, feat_size, 3]
if self._feature_type:
fbank = feat_lib.add_delta_delta(feat,
self._feature_size,
order=2)
if self._input_channels == 1:
fbank = fbank[:, :, 0:1]
else:
fbank = feat_lib.delta_delta(feat)
for label, seg, clip_id in examples:
feat = fbank
#logging.info("feat shape: {}".format(feat.shape))
seg = list(map(self.sample_to_frame, seg))
if seg[2]:
# need padding
feat = np.pad(feat, [(0, seg[2]), (0, 0), (0, 0)],
mode='constant')
feat = feat[seg[0]:seg[1], :, :]
assert len(feat) == self.sample_to_frame(
self.example_len), "{} {} {} {} {} {}".format(
filename, seg, len(feat), self.example_len,
self.sample_to_frame(self.example_len), seg[2])
if self.use_distilling:
soft_label = self.teacher(feat)
else:
class_num = self.taskconf['classes']['num']
soft_label = [0] * class_num
# convert string label to int label
labelid = self.class_id(label)
if use_text:
if clip_id == 0:
# only add into batch when meet the first clip
batch.append((feat, text2id, labelid, filename,
clip_id, soft_label))
else:
batch.append((feat, text2id, labelid, filename,
clip_id, soft_label))
#if i % 100000:
# logging.info('epoch:{} iter exmaple:{} total:{} : {:.2f}%'.format(
# self._epoch, i, total, i * 100 / total))
for inputs, texts, label, filepath, clip_id, soft_label in batch:
yield inputs, texts, label, filepath, clip_id, soft_label
batch.clear()
logging.info("Out of range")
raise StopIteration #pylint: disable=stop-iteration-return