Example #1
0
def vad_hmm_train(audio_list,
                  annotations_dir,
                  model_list,
                  feature_type,
                  n_coeffs_per_frame,
                  acc_frames,
                  working_dir,
                  apply_hlda=False,
                  hlda_nuisance_dims=0,
                  n_states=10,
                  n_gmm_comps=None,
                  acc_frame_shift=1,
                  n_train_iterations=10,
                  samp_period=0.01):
    logging.info('Number of hmm states: ' + str(n_gmm_comps))

    features_dir = os.path.join(working_dir, feature_type)
    acc_features_dir = features_dir + '_frames'
    fea_file_list = os.path.join(working_dir, 'feature_files.list')
    if not os.path.exists(features_dir):
        os.makedirs(features_dir)

    # Feature extraction
    vef.fea_extract(audio_list, feature_type, n_coeffs_per_frame, features_dir,
                    samp_period)
    vef.create_corresponding_list_assert(audio_list, features_dir,
                                         fea_file_list, 'fea')
    if not os.path.exists(acc_features_dir):
        os.makedirs(acc_features_dir)
    vef.accumulate_feature_vectors_parallel(fea_file_list, acc_frames,
                                            acc_features_dir, acc_frame_shift)
    vef.create_corresponding_list_assert(audio_list, acc_features_dir,
                                         fea_file_list, 'fea')

    # GMM training
    if n_gmm_comps == None:
        raise ValueError(
            'Automatic determination of the number of the GMM components not implemented'
        )

    model_dir = os.path.join(working_dir, 'models')
    train_gmm_classifier.train_hmm_classifier_incrementally(
        n_gmm_comps, n_states, n_coeffs_per_frame, feature_type, fea_file_list,
        model_list, annotations_dir, model_dir, apply_hlda, hlda_nuisance_dims,
        n_train_iterations)
    model_files = []
    m_f = os.path.join(model_dir, 'newMacros')
    model_files.append(m_f)
    m_f_no_hlda = m_f + "_no_hlda"
    model_files.append(m_f_no_hlda)

    assert (os.path.exists(m_f))
    return model_files
Example #2
0
def vad_gmm_list(audio_list, model_list, model_file, feature_type, 
            n_coeffs_per_frame, acc_frames, results_dir, working_dir, samp_period, win_length):
    features_dir = os.path.join(working_dir,feature_type)
    acc_features_dir = features_dir+'_frames'
    fea_file_list = os.path.join(working_dir,'feature_files.list')
    if not os.path.exists(features_dir):
        os.makedirs(features_dir)
    if not os.path.exists(acc_features_dir):
        os.makedirs(acc_features_dir)
    vef.fea_extract(audio_list,feature_type,n_coeffs_per_frame,features_dir,samp_period, win_length)
    vef.create_corresponding_list_assert(audio_list,features_dir,fea_file_list,'fea')
    vef.accumulate_feature_vectors(fea_file_list,acc_frames,acc_features_dir)
    vef.create_corresponding_list_assert(audio_list,acc_features_dir,fea_file_list,'fea')
      
    test_gmm_classifier(fea_file_list, model_list, model_file, results_dir)    
def estimate_channel_gmm(audio_list, model_list, model_file, feature_type, 
                         n_coeffs_per_frame, acc_frames, results_dir, working_dir):
    features_dir = os.path.join(working_dir,feature_type)
    acc_features_dir = features_dir+'_frames'
    fea_file_list = os.path.join(working_dir,'feature_files.list')
    if not os.path.exists(features_dir):
        os.makedirs(features_dir)
    if not os.path.exists(acc_features_dir):
        os.makedirs(acc_features_dir)
    vef.fea_extract(audio_list,feature_type,n_coeffs_per_frame,features_dir)
    vef.create_corresponding_list_assert(audio_list,features_dir,fea_file_list,'fea')
    vef.accumulate_feature_vectors(fea_file_list,acc_frames,acc_features_dir)
    vef.create_corresponding_list_assert(audio_list,acc_features_dir,fea_file_list,'fea')
      
    test_gmm_classifier.test_gmm_classifier(test_list=fea_file_list, model_list=model_list, 
                                            model_file=model_file, results_dir=results_dir, 
                                            mode='single')    
Example #4
0
def vad_gmm_list(audio_list, model_list, model_file, feature_type,
                 n_coeffs_per_frame, acc_frames, results_dir, working_dir,
                 samp_period, win_length):
    features_dir = os.path.join(working_dir, feature_type)
    acc_features_dir = features_dir + '_frames'
    fea_file_list = os.path.join(working_dir, 'feature_files.list')
    if not os.path.exists(features_dir):
        os.makedirs(features_dir)
    if not os.path.exists(acc_features_dir):
        os.makedirs(acc_features_dir)
    vef.fea_extract(audio_list, feature_type, n_coeffs_per_frame, features_dir,
                    samp_period, win_length)
    vef.create_corresponding_list_assert(audio_list, features_dir,
                                         fea_file_list, 'fea')
    vef.accumulate_feature_vectors(fea_file_list, acc_frames, acc_features_dir)
    vef.create_corresponding_list_assert(audio_list, acc_features_dir,
                                         fea_file_list, 'fea')

    test_gmm_classifier(fea_file_list, model_list, model_file, results_dir)
def vad_gmm_train(audio_list, annotations_dir, model_list, feature_type,
                          n_coeffs_per_frame, acc_frames, working_dir, apply_hlda=False,
                          hlda_nuisance_dims=0, n_gmm_comps=None, acc_frame_shift=1,
                          n_train_iterations=10, samp_period=0.01, win_length=0.025, segment_boundaries=[1/3.0, 2/3.0]):
    logging.info('Number of gmm components: '+str(n_gmm_comps))

    features_dir = os.path.join(working_dir,feature_type)
    acc_features_dir = features_dir+'_frames'
    fea_file_list = os.path.join(working_dir,'feature_files.list')
    if not os.path.exists(features_dir):
        os.makedirs(features_dir)

    # Feature extraction
    vef.fea_extract(audio_list, feature_type, n_coeffs_per_frame, features_dir, samp_period, win_length)
    vef.create_corresponding_list_assert(audio_list, features_dir, fea_file_list, 'fea')
    if not os.path.exists(acc_features_dir):
        os.makedirs(acc_features_dir)
    vef.accumulate_feature_vectors_parallel(fea_file_list, acc_frames,
                                            acc_features_dir, acc_frame_shift)
    vef.create_corresponding_list_assert(audio_list,acc_features_dir,fea_file_list,'fea')
    #vef.split_feature_files_labels(fea_file_list, annotations_dir, label_list,
    #                               boundaries=segment_boundaries)

    # GMM training
    if n_gmm_comps==None:
        raise ValueError('Automatic determination of the number of the GMM components not implemented')

    model_dir = os.path.join(working_dir,'models')
    train_gmm_classifier.train_M_sized_gmm_classifier_incrementally(n_gmm_comps, n_coeffs_per_frame,
                                                                    feature_type, fea_file_list, model_list,
                                                                    annotations_dir, model_dir, apply_hlda,
                                                                    hlda_nuisance_dims, n_train_iterations)
    model_files = []
    m_f = os.path.join(model_dir,'newMacros')
    model_files.append(m_f)
    m_f_no_hlda = m_f+"_no_hlda"
    model_files.append(m_f_no_hlda)

    assert(os.path.exists(m_f))
    return model_files
def estimate_channel_gmm(audio_list, model_list, model_file, feature_type,
                         n_coeffs_per_frame, acc_frames, results_dir,
                         working_dir):
    features_dir = os.path.join(working_dir, feature_type)
    acc_features_dir = features_dir + '_frames'
    fea_file_list = os.path.join(working_dir, 'feature_files.list')
    if not os.path.exists(features_dir):
        os.makedirs(features_dir)
    if not os.path.exists(acc_features_dir):
        os.makedirs(acc_features_dir)
    vef.fea_extract(audio_list, feature_type, n_coeffs_per_frame, features_dir)
    vef.create_corresponding_list_assert(audio_list, features_dir,
                                         fea_file_list, 'fea')
    vef.accumulate_feature_vectors(fea_file_list, acc_frames, acc_features_dir)
    vef.create_corresponding_list_assert(audio_list, acc_features_dir,
                                         fea_file_list, 'fea')

    test_gmm_classifier.test_gmm_classifier(test_list=fea_file_list,
                                            model_list=model_list,
                                            model_file=model_file,
                                            results_dir=results_dir,
                                            mode='single')