How to use the sidekit.FeaturesServer function in SIDEKIT

def train_ubm(self, feature_dir, speaker_list, ubm_list, distrib_nb, feature_extension='h5', num_threads=10):
        '''
        training the GMM with EM-Algorithm
        '''

        self.logger.info('training UBM')

        fs = sidekit.FeaturesServer(
            feature_filename_structure=(
                "{dir}/{speaker_list}/feat/{{}}.{ext}".format(dir=feature_dir, speaker_list=speaker_list,
                                                              ext=feature_extension)),
            dataset_list=["energy", "cep", "vad"],
            mask="[0-12]",
            feat_norm="cmvn",
            keep_all_features=True,
            delta=True,
            double_delta=True,
            rasta=True,
            context=None)

        ubm = sidekit.Mixture()
        llk = ubm.EM_split(fs, ubm_list, distrib_nb, num_thread=num_threads)
        ubm.write(get_experiment_nets()+'/ubm_{}.h5'.format(self.network_file))

feature_extension = 'h5'

        set_of_embeddings = []
        set_of_speakers = []
        set_of_num_embeddings = []
        set_of_times=[]
        checkpoints=["/TV_{}".format(self.network_file)]

        #load data:
        ubm = sidekit.Mixture()
        ubm.read(get_experiment_nets()+'/ubm_{}.h5'.format(self.network_file))
        ubm_list, test_list_long = self.load_data(speaker_list,os.path.splitext(os.path.split(self.get_validation_train_data())[1])[0])
        ubm_list, test_list_short = self.load_data(speaker_list,os.path.splitext(os.path.split(self.get_validation_test_data())[1])[0])
        tv, tv_mean, tv_sigma = sidekit.sidekit_io.read_tv_hdf5(get_experiment_nets()+"/TV_{}".format(self.network_file))

        fs = sidekit.FeaturesServer(feature_filename_structure=(
                "{dir}/{speaker_list}/feat/{{}}.{ext}".format(dir=get_training('i_vector'), speaker_list=speaker_list,
                                                              ext=feature_extension)),
            dataset_list=["energy", "cep", "vad"],
            mask="[0-12]",
            feat_norm="cmvn",
            keep_all_features=True,
            delta=True,
            double_delta=True,
            rasta=True,
            context=None)


        #exract ivectors
        test_stat_long = sidekit.StatServer(test_list_long, ubm=ubm, distrib_nb=distrib_nb, feature_size=0, index=None)
        test_stat_long.accumulate_stat(ubm=ubm, feature_server=fs, seg_indices=range(test_stat_long.segset.shape[0]),
                                  num_thread=nbThread)

Returns:
            server: which is the FeatureServer object
        """
        if group:
            feat_dir = os.path.join(self.BASE_DIR, "feat", group)
        else:
            feat_dir = os.path.join(self.BASE_DIR, "feat")
        # feature_filename_structure: structure of the filename to use to load HDF5 files
        # dataset_list: string of the form ["cep", "fb", vad", energy", "bnf"]
        # feat_norm: type of normalization to apply as post-processing
        # delta: if True, append the first order derivative
        # double_delta: if True, append the second order derivative
        # rasta: if True, perform RASTA filtering
        # keep_all_features: boolean, if True, keep all features; if False,
        #       keep frames according to the vad labels
        server = sidekit.FeaturesServer(
                feature_filename_structure=os.path.join(feat_dir, "{}.h5"),
                dataset_list=self.conf['features'],
                feat_norm="cmvn", #cepstral mean-variance normalization
                delta=True,
                double_delta=True,
                rasta=True,
                keep_all_features=True)
        logging.info("Feature-Server is created")
        logging.debug(server)
        return server