def params(cls, config=None):
""" Set params.
Args:
config: contains the following four optional parameters:
'type': Type of Opration. (string, default = 'CMVN')
'global_mean': Global mean of features. (float, default = 0.0)
'global_variance': Global variance of features. (float, default = 1.0)
'local_cmvn': If ture, local cmvn will be done on features. (bool, default = False)
Note:
Return an object of class HParams, which is a set of hyperparameters as
name-value pairs.
"""
hparams = HParams(cls=cls)
hparams.add_hparam("type", "CMVN")
hparams.add_hparam("global_mean", [0.0])
hparams.add_hparam("global_variance", [1.0])
hparams.add_hparam("local_cmvn", False)
if config is not None:
hparams.parse(config, True)
assert len(hparams.global_mean) == len(
hparams.global_variance
), "Error, global_mean length {} is not equals to global_variance length {}".format(
len(hparams.global_mean), len(hparams.global_variance)
)
hparams.global_variance = (np.sqrt(hparams.global_variance) + 1e-6).tolist()
return hparams
def params(cls, config=None):
""" set params """
hparams = HParams(cls=cls)
hparams.add_hparam("type", "CMVN")
hparams.add_hparam("global_mean", [0.0])
hparams.add_hparam("global_variance", [1.0])
hparams.add_hparam("local_cmvn", False)
if config is not None:
hparams.parse(config, True)
assert len(hparams.global_mean) == len(
hparams.global_variance
), "Error, global_mean length {} is not equals to global_variance length {}".format(
len(hparams.global_mean), len(hparams.global_variance))
hparams.global_variance = (np.sqrt(hparams.global_variance) +
1e-6).tolist()
return hparams