(feats, epsilon=1e-9)
return feats
feats: (NHWC)
feats: (NHWC)
feats : ( NHWC )
def apply_local_cmvn(feats, epsilon=1e-9):
  ''' feats: (NHWC) '''
  mean = tf.expand_dims(keras_backend.mean(feats, axis=1), axis=1)
  var = tf.expand_dims(keras_backend.var(feats, axis=1), axis=1)
  feats = (feats - mean) * tf.rsqrt(var + epsilon)
  return feats
def apply_local_cmvn ( feats , epsilon = 1e-9 ) : mean = tf . expand_dims ( keras_backend . mean ( feats , axis = 1 ) , axis = 1 ) var = tf . expand_dims ( keras_backend . var ( feats , axis = 1 ) , axis = 1 ) feats = ( feats - mean ) * tf . rsqrt ( var + epsilon ) return feats