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datasets/timit.py
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import tables
from collections import OrderedDict
from fuel.datasets.hdf5 import PytablesDataset
from utils import make_local_copy
class Timit(PytablesDataset):
"""TIMIT dataset.
Parameters
----------
which_set : str, opt
either 'train', 'dev' or 'test'.
alignment : bool
Whether return alignment.
features : str
The features to use. They will lead to the correct h5 file.
"""
def __init__(self, which_set='train', local_copy=False, **kwargs):
#self.path = '/home/parcollt/projects/rpp-bengioy/parcollt/Deep-Quaternary-Convolutional-Neural-Networks/TIMIT/timit_fbank_energy_deltas.h5'
self.path = '/u/parcollt/WORKSPACE/QCNN/Deep-Quaternary-Convolutional-Neural-Networks/TIMIT/timit_fbank_energy_deltas.h5'
#self.path = '/Users/titouanparcollet/CloudStation/LABO/WORKSPACE/EXPS/QCNN/timit_fbank_energy_deltas.h5'
if local_copy and not self.path.startswith('/Tmp'):
self.path = make_local_copy(self.path)
self.which_set = which_set
self.sources = ('features', 'features_shapes', 'phonemes')
super(Timit, self).__init__(
self.path, self.sources, data_node=which_set, **kwargs)
def get_phoneme_dict(self):
phoneme_list = self.h5file.root._v_attrs.phones_list
return OrderedDict(enumerate(phoneme_list))
def get_phoneme_ind_dict(self):
phoneme_list = self.h5file.root._v_attrs.phones_list
return OrderedDict(zip(phoneme_list, range(len(phoneme_list))))
def get_normalization_factors(self):
means = self.h5file.root._v_attrs.means
stds = self.h5file.root._v_attrs.stds
return means, stds
def open_file(self, path):
self.h5file = tables.open_file(path, mode="r")
node = self.h5file.get_node('/', self.data_node)
self.nodes = [getattr(node, source) for source in self.sources_in_file]
if self.stop is None:
self.stop = self.nodes[0].nrows
self.num_examples = self.stop - self.start
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