comparison Code/genre_classification/learning/preprocess_spectrograms_gtzan.py @ 24:68a62ca32441

Organized python scripts

+# -*- coding: utf-8 -*-
+"""
+Created on Thu Jul 23 21:55:58 2015
+
+@author: paulochiliguano
+"""
+
+
+import tables
+import numpy as np
+import cPickle
+import sklearn.preprocessing as preprocessing
+
+#Read HDF5 file that contains log-mel spectrograms
+filename = '/homes/pchilguano/msc_project/dataset/gtzan/features/\
+feats_3sec_9.h5'
+with tables.openFile(filename, 'r') as f:
+    features = f.root.x.read()
+    #filenames = f.root.filenames.read()
+
+#Pre-processing of spectrograms mean=0 and std=1
+#initial_shape = features.shape[1:]
+n_per_example = np.prod(features.shape[1:-1])
+number_of_features = features.shape[-1]
+flat_data = features.view()
+flat_data.shape = (-1, number_of_features)
+scaler = preprocessing.StandardScaler().fit(flat_data)
+flat_data = scaler.transform(flat_data)
+flat_data.shape = (features.shape[0], -1)
+#flat_targets = filenames.repeat(n_per_example)
+#genre = np.asarray([line.strip().split('\t')[1] for line in open(filename,'r').readlines()])
+
+#Read labels from ground truth
+filename = '/homes/pchilguano/msc_project/dataset/gtzan/lists/ground_truth.txt'
+with open(filename, 'r') as f:
+    tag_set = set()
+    for line in f:
+        tag = line.strip().split('\t')[1]
+        tag_set.add(tag)
+
+#Assign label to a discrete number
+tag_dict = dict([(item, index) for index, item in enumerate(sorted(tag_set))])
+with open(filename, 'r') as f:
+    target = np.asarray([], dtype='int32')
+    mp3_dict = {}
+    for line in f:
+        tag = line.strip().split('\t')[1]
+        target = np.append(target, tag_dict[tag])
+
+train_input, valid_input, test_input = np.array_split(
+    flat_data,
+    [flat_data.shape[0]*1/2,
+    flat_data.shape[0]*3/4]
+)
+train_target, valid_target, test_target = np.array_split(
+    target,
+    [target.shape[0]*1/2,
+    target.shape[0]*3/4]
+)
+
+f = file('/homes/pchilguano/msc_project/dataset/gtzan/features/\
+gtzan_3sec_9.pkl', 'wb')
+cPickle.dump(
+    (
+        (train_input, train_target),
+        (valid_input, valid_target),
+        (test_input, test_target)
+    ),
+    f,
+    protocol=cPickle.HIGHEST_PROTOCOL
+)
+f.close()
+
+'''
+flat_target = target.repeat(n_per_example)
+
+train_input, valid_input, test_input = np.array_split(flat_data, [flat_data.shape[0]*4/5, flat_data.shape[0]*9/10])
+train_target, valid_target, test_target = np.array_split(flat_target, [flat_target.shape[0]*4/5, flat_target.shape[0]*9/10])
+
+f = file('/homes/pchilguano/deep_learning/gtzan_logistic.pkl', 'wb')
+cPickle.dump(((train_input, train_target), (valid_input, valid_target), (test_input, test_target)), f, protocol=cPickle.HIGHEST_PROTOCOL)
+f.close()
+'''