def __init__(self, config, mode):
super().__init__(config, mode)
self.dummy = config['data']['task']['dummy']
self.batch_mode = config['data']['task']['batch_mode']
self.batch_size = config['solver']['optimizer']['batch_size']
self._shuffle_buffer_size = config['data']['task'][
'shuffle_buffer_size']
self._need_shuffle = config['data']['task']['need_shuffle']
# get batches form data path
if self.dummy:
self._feat_shape = [40]
logging.info("Dummy data: feat {}".format(self.feat_shape))
self._vocab_size = 100
else:
data_metas = espnet_utils.get_batches(self.config, mode)
self.batches = data_metas['data']
self.n_utts = data_metas['n_utts']
logging.info("utts: {}".format(self.n_utts))
# [nframe, feat_shape, ...]
self._feat_shape = self.batches[0][0][1]['input'][0]['shape'][1:]
# [tgt_len, vocab_size]
self._vocab_size = self.batches[0][0][1]['output'][0]['shape'][1]
logging.info('#input feat shape: ' + str(self.feat_shape))
logging.info('#output dims: ' + str(self.vocab_size))
self._converter = espnet_utils.ASRConverter(self.config)
def test_converter(self):
np.random.seed(100)
nexamples_list = (10, 12)
batch_size = 4
self.config['solver']['optimizer']['batch_size'] = batch_size
converter = espnet_utils.ASRConverter(self.config)
for mode in (utils.TRAIN, utils.EVAL, utils.INFER):
for nexamples in nexamples_list:
desire_xs, desire_ilens, desire_ys, desire_olens = generate_json_data(
self.config, mode, nexamples)
del desire_xs, desire_ilens, desire_ys, desire_olens
data_metas = espnet_utils.get_batches(self.config, mode)
batches = data_metas['data']
n_utts = data_metas['n_utts']
self.assertEqual(n_utts, nexamples)
o_uttids = []
o_xs = []
o_ilens = []
o_ys = []
o_olens = []
for _, batch in enumerate(batches):
batch_data = converter(batch)
self.assertEqual(len(batch_data), 5)
xs, ilens, ys, olens, uttids = batch_data
for x, ilen, y, olen, uttid in zip(xs, ilens, ys, olens,
uttids):
self.assertDTypeEqual(x, np.float32)
self.assertDTypeEqual(y, np.int64)
o_uttids.append(uttid)
o_xs.append(x)
o_ilens.append(ilen)
o_ys.append(y)
o_olens.append(olen)
self.assertEqual(len(o_xs), nexamples)
