# X_train = np.reshape(X_train, ( len(X_train_transformed), num_features ) ) print X_train.shape # for filename, lbl, features in X_train_transformed: # if features is None: # print "Train",filename, "is none" # continue # if X_total is not None: # X_total = np.vstack([ X_total, features ]) # else: # X_total = features X_total = X_train print "Ready with train" print "Starting train" test_data = get_all_test_data() filename_by_path = {path: filename for (path, filename) in test_data} X_test_transformed = load_test_features("bow_test_features.pcl", extract_mfcc_features, limit=LIMIT) X_test = np.vstack([f for (_, _, f) in X_test_transformed if f is not None]) # for filename, lbl, features in X_test_transformed: # if features is not None: # X_total = np.vstack([ X_total, features ]) # else: # print "Null test", filename X_total = np.vstack([X_total, X_test]) print X_total.shape print "Ready with test" print "Loading voxforge features" if VOX_FEATURES: if LIMIT is None: vox_limit = 4000 else:
__author__ = 'egor' VALIDATE = True np.random.seed(100500) VOX_FEATURES = True LIMIT = 5000 print "Using Vox features", VOX_FEATURES print "LIMIT", LIMIT X_train_transformed = load_train_features('gmm_train_features.pcl', extract_gmm_feature, limit = LIMIT ) print "Loaded train" test_data = get_all_test_data() filename_by_path = {path : filename for (path, filename) in test_data } X_test_transformed = load_test_features('gmm_test_features.pcl', extract_gmm_feature, limit = LIMIT) print "Loaded test" if VOX_FEATURES: print "Loading voxforge features" if LIMIT is None: vox_limit = 4000 else: vox_limit = LIMIT voxforge_features = load_vox_forge_files('/store/egor/voxforge', vox_limit, 'gmm_voxfeatures.pcl', extract_gmm_feature ) else: voxforge_features = [] #X = np.array([features for (_,_, features) in itertools.chain(X_train_transformed, voxforge_features)]) X = np.array([features for (_,_, features) in itertools.chain(X_train_transformed, voxforge_features) if features is not None ]) y = np.array([lbl for (_,lbl, _) in itertools.chain(X_train_transformed, voxforge_features) if lbl is not None ])