class ASRConverter(Converter):
''' ASR preprocess '''
def __init__(self, config):
super().__init__(config)
taskconf = self.config['data']['task']
assert taskconf['type'] == TASK_SET['asr']
self.subsampling_factor = taskconf['src']['subsampling_factor']
self.preprocess_conf = taskconf['src']['preprocess_conf']
# mode: asr or tts
self.load_inputs_and_targets = LoadInputsAndTargets(
mode=taskconf['type'],
load_output=True,
preprocess_conf=self.preprocess_conf)
#pylint: disable=arguments-differ
#pylint: disable=too-many-branches
def transform(self, batch):
"""Function to load inputs, targets and uttid from list of dicts
:param List[Tuple[str, dict]] batch: list of dict which is subset of
loaded data.json
:return: list of input token id sequences [(L_1), (L_2), ..., (L_B)]
:return: list of input feature sequences
[(T_1, D), (T_2, D), ..., (T_B, D)]
:rtype: list of float ndarray
:return: list of target token id sequences [(L_1), (L_2), ..., (L_B)]
:rtype: list of int ndarray
Reference: Espnet source code, /espnet/utils/io_utils.py
https://github.com/espnet/espnet/blob/master/espnet/utils/io_utils.py
"""
x_feats_dict = OrderedDict() # OrderedDict[str, List[np.ndarray]]
y_feats_dict = OrderedDict() # OrderedDict[str, List[np.ndarray]]
uttid_list = [] # List[str]
mode = self.load_inputs_and_targets.mode
for uttid, info in batch:
uttid_list.append(uttid)
if self.load_inputs_and_targets.load_input:
# Note(kamo): This for-loop is for multiple inputs
for idx, inp in enumerate(info['input']):
# {"input":
# [{"feat": "some/path.h5:F01_050C0101_PED_REAL",
# "filetype": "hdf5",
# "name": "input1", ...}], ...}
#pylint: disable=protected-access
x_data = self.load_inputs_and_targets._get_from_loader(
filepath=inp['feat'],
filetype=inp.get('filetype', 'mat'))
x_feats_dict.setdefault(inp['name'], []).append(x_data)
elif mode == 'tts' and self.load_inputs_and_targets.use_speaker_embedding:
for idx, inp in enumerate(info['input']):
if idx != 1 and len(info['input']) > 1:
x_data = None
else:
x_data = self.load_inputs_and_targets._get_from_loader( #pylint: disable=protected-access
filepath=inp['feat'],
filetype=inp.get('filetype', 'mat'))
x_feats_dict.setdefault(inp['name'], []).append(x_data)
if self.load_inputs_and_targets.load_output:
for idx, inp in enumerate(info['output']):
if 'tokenid' in inp:
# ======= Legacy format for output =======
# {"output": [{"tokenid": "1 2 3 4"}])
x_data = np.fromiter(map(int, inp['tokenid'].split()),
dtype=np.int64)
else:
# ======= New format =======
# {"input":
# [{"feat": "some/path.h5:F01_050C0101_PED_REAL",
# "filetype": "hdf5",
# "name": "target1", ...}], ...}
x_data = self.load_inputs_and_targets._get_from_loader( #pylint: disable=protected-access
filepath=inp['feat'],
filetype=inp.get('filetype', 'mat'))
y_feats_dict.setdefault(inp['name'], []).append(x_data)
if self.load_inputs_and_targets.mode == 'asr':
#pylint: disable=protected-access
return_batch, uttid_list = self.load_inputs_and_targets._create_batch_asr(
x_feats_dict, y_feats_dict, uttid_list)
elif self.load_inputs_and_targets.mode == 'tts':
_, info = batch[0]
eos = int(info['output'][0]['shape'][1]) - 1
#pylint: disable=protected-access
return_batch, uttid_list = self.load_inputs_and_targets._create_batch_tts(
x_feats_dict, y_feats_dict, uttid_list, eos)
else:
raise NotImplementedError
if self.load_inputs_and_targets.preprocessing is not None:
# Apply pre-processing only to input1 feature, now
if 'input1' in return_batch:
return_batch['input1'] = \
self.load_inputs_and_targets.preprocessing(return_batch['input1'], uttid_list,
**self.load_inputs_and_targets.preprocess_args)
# Doesn't return the names now.
return tuple(return_batch.values()), uttid_list
