def train_plda(iv_file, train_list, val_list, preproc_file, epochs, ml_md, md_epochs, output_path, **kwargs): if preproc_file is not None: preproc = TransformList.load(preproc_file) else: preproc = None vcr_args = VCR.filter_args(**kwargs) vcr_train = VCR(iv_file, train_list, preproc, **vcr_args) x, class_ids = vcr_train.read() x_val = None class_ids_val = None if val_list is not None: vcr_val = VCR(iv_file, val_list, preproc, **vcr_args) x_val, class_ids_val = vcr_val.read() t1 = time.time() plda_args = F.filter_train_args(**kwargs) model = F.create_plda(**plda_args) elbos = model.fit(x, class_ids, x_val=x_val, class_ids_val=class_ids_val, epochs=epochs, ml_md=ml_md, md_epochs=md_epochs) logging.info('Elapsed time: %.2f s.' % (time.time()-t1)) model.save(output_path) elbo = np.vstack(elbos) num = np.arange(epochs) elbo = np.vstack((num, elbo)).T elbo_path=os.path.splitext(output_path)[0] + '.csv' np.savetxt(elbo_path, elbo, delimiter=',')
def train_be(iv_file, train_list, adapt_iv_file, adapt_list, lda_dim, plda_type, y_dim, z_dim, epochs, ml_md, md_epochs, w_mu, w_B, w_W, output_path, **kwargs): # Read data logging.info('loading data') vcr_args = VCR.filter_args(**kwargs) vcr_train = VCR(iv_file, train_list, None, **vcr_args) x, class_ids = vcr_train.read() # Train LDA logging.info('train LDA') t1 = time.time() lda = LDA(lda_dim=lda_dim, name='lda') lda.fit(x, class_ids) x_lda = lda.predict(x) logging.info('LDA elapsed time: %.2f s.' % (time.time() - t1)) # Train centering and whitening logging.info('train length norm') t1 = time.time() lnorm = LNorm(name='lnorm') lnorm.fit(x_lda) x_ln = lnorm.predict(x_lda) logging.info('length norm elapsed time: %.2f s.' % (time.time() - t1)) # Train PLDA logging.info('train PLDA') t1 = time.time() plda = F.create_plda(plda_type, y_dim=y_dim, z_dim=z_dim, name='plda') elbo = plda.fit(x_ln, class_ids, epochs=epochs, ml_md=ml_md, md_epochs=md_epochs) logging.info('PLDA elapsed time: %.2f s.' % (time.time() - t1)) # Save models logging.info('saving models') preproc = TransformList(lda) preproc.append(lnorm) if not os.path.exists(output_path): os.makedirs(ouput_path) preproc.save(output_path + '/lda_lnorm.h5') plda.save(output_path + '/plda.h5') num = np.arange(epochs) elbo = np.vstack((num, elbo)).T np.savetxt(output_path + '/elbo.csv', elbo, delimiter=',') #adaptation vcr = VCR(adapt_iv_file, adapt_list, None) x, class_ids = vcr.read() x_lda = lda.predict(x) lnorm.update_T = False lnorm.fit(x_lda) preproc = TransformList(lda) preproc.append(lnorm) preproc.save(output_path + '/lda_lnorm_adapt.h5') x_ln = lnorm.predict(x_lda) plda_adapt = plda.copy() elbo = plda.fit(x_ln, class_ids, epochs=epochs) plda_adapt.weighted_avg_model(plda, w_mu, w_B, w_W) plda_adapt.save(output_path + '/plda_adapt.h5') num = np.arange(epochs) elbo = np.vstack((num, elbo)).T np.savetxt(output_path + '/elbo_adapt.csv', elbo, delimiter=',')