def preprocess(self, inputs):
''' Speech preprocessing. '''
with tf.variable_scope('feature'):
if self.input_type == 'samples':
# FIXME: stub
feats = None
else:
if 'cmvn_type' in self.audioconf:
cmvn_type = self.audioconf['cmvn_type']
else:
cmvn_type = 'global'
logging.info('cmvn_type: %s' % (cmvn_type))
if cmvn_type == 'global':
self.mean, self.std = utils.load_cmvn(
self.audioconf['cmvn_path'])
feats = utils.apply_cmvn(inputs, self.mean, self.std)
elif cmvn_type == 'local':
feats = utils.apply_local_cmvn(inputs)
elif cmvn_type == 'sliding':
raise ValueError('cmvn_type %s not implemented yet.' %
(cmvn_type))
elif cmvn_type == 'none':
feats = inputs
else:
raise ValueError('Error cmvn_type %s.' % (cmvn_type))
return feats
def testApplyLocalCmvn(self): #pylint: disable=invalid-name
''' test apply_local_cmvn() '''
np.random.seed(12)
tf.set_random_seed(12)
feat_size = 40
delta_deltas = True
feat_shape = [2, 10, feat_size, 3 if delta_deltas else 1]
feat = np.random.randn(*feat_shape)
feat = feat.astype(np.float32)
mean = np.mean(feat, axis=1, keepdims=True)
var = np.var(feat, axis=1, keepdims=True)
eps = 1e-9
feat_true = (feat - mean) / np.sqrt(var + eps)
feat = tf.constant(feat)
feat_out = utils.apply_local_cmvn(feat, epsilon=eps)
with self.cached_session(use_gpu=False, force_gpu=False):
self.assertAllClose(feat_out.eval(), feat_true)
