//  [python]
def train_gmm(data_label, features, train_keys, train_folders, audio_ext, dict_file, ncomp, init_only=False):
    logging.info('Start GMM training.')

    partial_gmm_dict_file = '_'.join((dict_file, data_label, 'init', 'partial.pkl'))
    #print('~~~~~~~~~')
    #print(partial_gmm_dict_file)
    if exists(partial_gmm_dict_file):
        gmm = mixture.GaussianMixture(covariance_type='diag')
        with open(partial_gmm_dict_file, "rb") as tf:
            gmm._set_parameters(pickle.load(tf))
    else:
        data = list()
        for k, train_key in enumerate(train_keys):
            #print('---train_key----')
            #print(train_key)
            pd = pandas.read_csv(train_key, sep=' ', header=None)
            #print(pd)
            files = pd[pd[1] == data_label][2]
            #print('------')
            #print(files)
            # files_subset = sample(list(files), 1000)  # random init with 1000 files
            files_subset = (files.reset_index()[2]).loc[list(range(0, len(files), 10))]  # only every 10th file init
            for file in files_subset:
                # print('-----')
                # print(train_folders[k])
                # print(file)
                try:
                    Tx = extract_features(file, features=features, cached=True)
                    data.append(Tx.T)
                    #print(Tx.T)
                except:
                    print(file)

        #print(data)
        X = vstack(data)
        gmm = mixture.GaussianMixture(n_components=ncomp,
                              random_state=None,
                              covariance_type='diag',
                              max_iter=10,
                              verbose=2,
                              verbose_interval=1).fit(X)

        logging.info('GMM init done - llh: %.5f' % gmm.lower_bound_)

        with open(partial_gmm_dict_file, "wb") as f:
            pickle.dump(gmm._get_parameters(), f)

    if init_only:
        return gmm

    # EM training
    prev_lower_bound = -infty
    for i in range(10):
        partial_gmm_dict_file = '_'.join((dict_file, data_label, str(i), 'partial.pkl'))
        if exists(partial_gmm_dict_file):
            with open(partial_gmm_dict_file, "rb") as tf:
                gmm._set_parameters(pickle.load(tf))
                continue

        nk_acc = zeros_like(gmm.weights_)
        mu_acc = zeros_like(gmm.means_)
        sigma_acc = zeros_like(gmm.covariances_)
        log_prob_norm_acc = 0
        n_samples = 0
        for k, train_key in enumerate(train_keys):
            pd = pandas.read_csv(train_key, sep=' ', header=None)
            print('-133!!!!!---')
            print(pd)
            files = pd[pd[1] == data_label][2]

            for file in files.values:
                Tx = extract_features(train_folders[k] + file + audio_ext, features=features, cached=True)
                n_samples += Tx.shape[1]

                # e step
                weighted_log_prob = gmm._estimate_weighted_log_prob(Tx.T)
                log_prob_norm = logsumexp(weighted_log_prob, axis=1)
                with errstate(under='ignore'):
                    # ignore underflow
                    log_resp = weighted_log_prob - log_prob_norm[:, None]
                log_prob_norm_acc += log_prob_norm.sum()

                # m step preparation
                resp = exp(log_resp)
                nk_acc += resp.sum(axis=0) + 10 * finfo(log(1).dtype).eps
                mu_acc += resp.T @ Tx.T
                sigma_acc += resp.T @ (Tx.T ** 2)

        # m step
        gmm.means_ = mu_acc / nk_acc[:, None]
        gmm.covariances_ = sigma_acc / nk_acc[:, None] - gmm.means_ ** 2 + gmm.reg_covar
        gmm.weights_ = nk_acc / n_samples
        gmm.weights_ /= gmm.weights_.sum()
        if (gmm.covariances_ <= 0.0).any():
            raise ValueError("ill-defined empirical covariance")
        gmm.precisions_cholesky_ = 1. / sqrt(gmm.covariances_)

        with open(partial_gmm_dict_file, "wb") as f:
            pickle.dump(gmm._get_parameters(), f)

        # infos
        lower_bound = log_prob_norm_acc / n_samples
        change = lower_bound - prev_lower_bound
        logging.info("  Iteration %d\t llh %.5f\t ll change %.5f" % (i, lower_bound, change))
        prev_lower_bound = lower_bound

        if abs(change) < gmm.tol:
            logging.info('  Coverged; too small change')
            gmm.converged_ = True
            break

    return gmm