Add Music Model Generation

Add the music model generation and usage scripts with documentation

MusicModelCreation/createMusicalFeaturesets.py

@@ -0,0 +1,110 @@
+'''
+Thomas Matlak, Avi Vajpeyi, Avery Rapson
+CS 310 Final Project
+
+Given textfiles with the musical notes in int format, this creates a pickle of
+the attributes and classes for all the musical data stored in the text files
+(each text file is for one class).
+
+The data is stored as frequencies of each note on a keyboard, and the class label
+is stored in 'one hot' format. 10 pre cent of data present set aside as testing data.
+
+
+Usage:
+python createMusicalFeaturesets.py
+
+OUTPUT: notesData.pickle
+    A pickle with the attributes and classes for music data
+    pickle data continas: train_attribute ,train_class, test_attribute, test_class
+
+NOTE: Need to update the follwoing depending on usage of script
+ROOT_DIR = root/directrory/where/text/reside
+DataFile = ["emotion1.txt","emotion2.txt"...])
+'''
+
+from mido import MidiFile, MidiTrack, Message
+import mido
+import random
+import pickle
+from collections import Counter
+import numpy as np
+import os
+
+
+'''
+Assume we have the following as our 'LEXICON'
+  unique word list : [chair, table, spoon, television]
+
+Assume this is our current sample data:
+  String: I pulled my chair up to the table
+
+Create a training vector that holds the count of each lexicon word:
+  training vector : [1, 1, 0, 0]
+  (since chair table are in string, but spoon TV arnt)
+
+
+Do this for all strings
+
+'''
+
+ROOT_DIR = "TrainingData/"
+DataFile = ["NegExamples/sadSongs.txt","PosExamples/happySongs.txt"]
+
+pianoSize = 128 # notes 0 - 127
+# this also defines our lexicon
+
+# larger dataset, more memory gets used up MemoryError
+def sample_handling(sample, classification):
+    featureset = []
+    '''
+    featureset =
+    [
+        [[0 1 0 0 1 0 0 ...], [1, 0]]
+        [[0 1 0 0 1 1 1 ...], [0, 1]]
+        ....
+    ]
+    so the first list is the array of matches with the lexicon
+    the second is which classification the features falls into (yes or no)
+    '''
+    with open(sample,'r') as f:
+        contents = f.readlines()
+        for l in contents:
+            notes = np.fromstring(l, dtype=int, sep=' ')
+            noteCount = np.zeros(pianoSize)
+            for note in notes:
+                noteCount[note] += 1
+            noteCount = list(noteCount)
+            featureset.append([noteCount, classification])
+    return featureset
+
+
+
+def create_feature_sets_and_labels(DataFile,test_size = 0.1):
+    features = []
+    features += sample_handling(ROOT_DIR+DataFile[0],[0,1])# neg
+    features += sample_handling(ROOT_DIR+DataFile[1],[1,0]) # pos
+    random.shuffle(features)
+    '''
+        does tf.argmax([output]) == tf.argmax([expectations]) will look like:
+                tf.argmax([55454, 342324]) == tf.argmax([1,0])
+    '''
+
+    features = np.array(features)
+    testing_size = int(test_size*len(features))
+    train_x = list(features[:,0][:-testing_size]) #[[5,8],[7,9]]  --> [:,0] does [5,7] (all of the 0 elememts) ie the labels in this case
+    train_y = list(features[:,1][:-testing_size])
+
+    test_x = list(features[:,0][-testing_size:])
+    test_y = list(features[:,1][-testing_size:])
+
+
+    return train_x,train_y,test_x,test_y
+
+
+if __name__ == '__main__':
+    train_x,train_y,test_x,test_y = create_feature_sets_and_labels(DataFile)
+    with open('notesData.pickle','wb') as f:
+        pickle.dump([train_x,train_y,test_x,test_y],f) # dump data as a list, into a file
+        # this saves the lexicon for pos and neg words
+        # every inputted value is converted to a lexicon saving this info
+        # a lot of memory!

MusicModelCreation/midiNoteSegmenter.py

@@ -0,0 +1,81 @@
+'''
+Thomas Matlak
+CS 310 Final Project
+
+Takes directory containing midi files as input, produces a text file containing only the midi note values for the first 10 seconds of each musical piece.
+
+Usage:
+python midiNoteSegments.py /path/to/midi/folder/ [/path/to/output/file.txt]
+'''
+
+import sys, glob
+from mido import MidiFile, MidiTrack, Message
+from keras.layers import LSTM, Dense, Activation, Dropout
+from keras.preprocessing import sequence
+from keras.models import Sequential
+from keras.optimizers import RMSprop
+from sklearn.preprocessing import MinMaxScaler
+import numpy as np
+import mido
+import csv
+
+indir = sys.argv[1]
+outfile_name = indir + "/out.txt"
+
+if 2 < len(sys.argv):
+    outfile_name = sys.argv[2]
+
+midi_files = glob.glob(indir + "/*.mid")
+
+transposition_intervals = {
+    'Cb': -11,
+    'Gb': -6,
+    'Db': -1,
+    'Ab': -8,
+    'Eb': -3,
+    'Bb': -10,
+    'F': -5,
+    'C': 0,
+    'G': -7,
+    'D': -2,
+    'A': -9,
+    'E': -4,
+    'B': -11,
+    'F#': -6,
+    'C#':-1
+}
+
+with open(outfile_name, 'wb') as outfile:
+    writer = csv.writer(outfile, delimiter=' ')
+
+    for midi_file in midi_files:
+        mid = MidiFile(midi_file)
+
+        notes = []
+
+        time = float(0)
+        prev = float(0)
+
+        key = "C"
+
+        for msg in mid:
+            if time >= 10:
+                break
+            ### this time is in seconds, not ticks
+            time += msg.time
+
+            if msg.type == "key_signature":
+                key = msg.key
+
+            if not msg.is_meta:
+                ### only interested in piano channel
+                if msg.channel == 0:
+                    if msg.type == 'note_on':
+                        # note in vector form to train on
+                        note = msg.bytes()
+                        # only interested in the note #and velocity. note message is in the form of [type, note, velocity]
+                        note = note[1] #:3]
+                        # note.append(time - prev)
+                        prev = time
+                        notes.append(note + transposition_intervals[key]) # this preserves the intervlas, but transposes a;; samples to C
+        writer.writerow(notes)

MusicModelCreation/trainMusicNN.py

@@ -0,0 +1,158 @@
+import tensorflow as tf
+import numpy as np
+import pickle
+import os
+# from tensorflow.examples.tutorials.mnist import input_data
+# mnist = input_data.read_data_sets("/tmp/data/", one_hot = True)
+# from createMusicalFeaturesets import create_feature_sets_and_labels
+train_x,train_y,test_x,test_y = pickle.load(open("notesData2.pickle", "rb"))
+
+
+saveFile = "savedModels/musicModelpy27"
+
+
+n_nodes_hl1 = 1000
+n_nodes_hl2 = 1000
+n_nodes_hl3 = 1000
+
+n_classes = 2
+batch_size = 10
+hm_epochs = 9
+
+input_data_size = len(train_x[0])# each train_x instance is one song, and so one lexicon of notes
+print("DEBUG: input data size = "+str(input_data_size))
+
+x = tf.placeholder('float')
+y = tf.placeholder('float')
+
+hidden_1_layer = {'f_fum':n_nodes_hl1,
+                  'weight':tf.Variable(tf.random_normal([128, n_nodes_hl1])),
+                  'bias':tf.Variable(tf.random_normal([n_nodes_hl1]))}
+
+hidden_2_layer = {'f_fum':n_nodes_hl2,
+                  'weight':tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
+                  'bias':tf.Variable(tf.random_normal([n_nodes_hl2]))}
+
+hidden_3_layer = {'f_fum':n_nodes_hl3,
+                  'weight':tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])),
+                  'bias':tf.Variable(tf.random_normal([n_nodes_hl3]))}
+
+output_layer = {'f_fum':None,
+                'weight':tf.Variable(tf.random_normal([n_nodes_hl3, n_classes])),
+                'bias':tf.Variable(tf.random_normal([n_classes])),}
+
+
+
+# Nothing changes
+def neural_network_model(data):
+    ####INPUT LAYER (HIDDEN LAYER 1)
+    l1 = tf.add(tf.matmul(data,hidden_1_layer['weight']), hidden_1_layer['bias'])
+    l1 = tf.nn.relu(l1)
+    ####HIDDEN LAYER 2
+    l2 = tf.add(tf.matmul(l1,hidden_2_layer['weight']), hidden_2_layer['bias'])
+    l2 = tf.nn.relu(l2)
+    ####HIDDEN LAYER 3
+    l3 = tf.add(tf.matmul(l2,hidden_3_layer['weight']), hidden_3_layer['bias'])
+    l3 = tf.nn.relu(l3)
+    ####OUTPUT LAYER
+    output = tf.matmul(l3,output_layer['weight']) + output_layer['bias']
+
+    return output
+
+
+def train_neural_network(x):
+    prediction = neural_network_model(x)
+    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y) )
+    optimizer = tf.train.AdamOptimizer().minimize(cost)
+    with tf.Session() as sess:
+        sess.run(tf.global_variables_initializer())
+        # try:
+        #     epoch = int(open(tf_log,'r').read().split('\n')[-2])+1
+        #     print('STARTING EPOCH:',epoch)
+        # except:
+        #     epoch = 1
+        batches_run = 0
+        epoch = 1
+        while epoch <= hm_epochs:
+            # if epoch != 1:
+            #     #saver.restore(sess,'/'+saveFile)
+            #     print("Should Restore Saved File")
+            epoch_loss = 1
+            i=0
+            while i < len(train_x):
+                start = i
+                end = i+batch_size
+                batch_x = np.array(train_x[start:end])
+                batch_y = np.array(train_y[start:end])
+                _, c = sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y})
+                epoch_loss += c
+                i+=batch_size
+                batches_run +=1
+                print('Batch run:',batches_run,'/',batch_size,'| Epoch:',
+                      epoch,'| Batch Loss:',c,)
+            saver.save(sess, saveFile)
+            print("Should Save session in "+ saveFile )
+            print('Epoch', epoch+1, 'completed out of',hm_epochs,'loss:', epoch_loss)
+            # with open(tf_log,'a') as f:
+            #     f.write(str(epoch)+'\n')
+            epoch +=1
+        correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
+        accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
+        print('Trained',len(train_x),'samples.')
+        print('Tested',len(test_x),'samples.')
+        accPercent = accuracy.eval({x:test_x, y:test_y})*100
+        print('Accuracy: '+ str(accPercent)+ '%')
+
+
+saver = tf.train.Saver()
+# tf_log = 'tf.log' ## SAVES EPOCH NUMBER
+train_neural_network(x)
+
+
+def test_neural_network():
+    prediction = neural_network_model(x)
+    with tf.Session() as sess:
+        sess.run(tf.global_variables_initializer())
+        # for epoch in range(hm_epochs):
+        #     try:
+        #         y =2
+        #         # saver.restore(sess,'/'+saveFile)
+        #         print("Restoring "+ saveFile )
+        #     except Exception as e:
+        #         print(str(e))
+        #     epoch_loss = 0
+
+        correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
+        accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
+
+        ## WHEN WE SAVE TESTING DATA SEPARATLY
+        # feature_sets = []
+        # labels = []
+        # counter = 0
+        # with open('processed-test-set.csv', buffering=20000) as f:
+        #     for line in f:
+        #         try:
+        #             features = list(eval(line.split('::')[0]))
+        #             label = list(eval(line.split('::')[1]))
+        #             feature_sets.append(features)
+        #             labels.append(label)
+        #             counter += 1
+        #         except:
+        #             pass
+
+        testx = np.array(test_x)
+        testy = np.array(test_y)
+        counter = len(test_x)
+        print(testx,testy)
+        print(test_x,test_y)
+        print('******RESULTS******')
+        print('Tested',counter,'samples.')
+        print('Accuracy:', accuracy.eval({x:testx, y:testy}) )
+
+
+#test_neural_network()
+
+
+print ("\n\n\nFINISHED\n\n\n")
+# x =os.remove("tf.log")
+# print("removed :" + str(x))

MusicModelCreation/usingMusicNN.py

@@ -0,0 +1,134 @@
+'''
+Thomas Matlak Avi Vajpeyi, Avery Rapson
+CS 310 Final Project
+
+Loads the NN saved in the dir 'savedFile'. The function predictmood(input_midi_file)
+takes a midi files in MIDO format and returns if it is happy or sad
+
+Usage:
+python usingMusicNN.py
+'''
+
+import tensorflow as tf
+import json
+from mido import MidiFile
+import numpy as np
+import tempfile
+
+
+midiFile =  "01.mid"
+saveFile = "savedModels/musicModelpy27"
+
+
+pianoSize = 128
+
+print("Bad ass Neural Net being loaded...")
+
+
+hm_data = 2000000
+
+
+n_nodes_hl1 = 1000
+n_nodes_hl2 = 1000
+n_nodes_hl3 = 1000
+
+n_classes = 2
+batch_size = 10
+hm_epochs = 9
+
+
+x = tf.placeholder('float')
+y = tf.placeholder('float')
+
+
+current_epoch = tf.Variable(1)
+
+hidden_1_layer = {'f_fum':n_nodes_hl1,
+                  'weight':tf.Variable(tf.random_normal([pianoSize, n_nodes_hl1])),
+                  'bias':tf.Variable(tf.random_normal([n_nodes_hl1]))}
+
+hidden_2_layer = {'f_fum':n_nodes_hl2,
+                  'weight':tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
+                  'bias':tf.Variable(tf.random_normal([n_nodes_hl2]))}
+
+hidden_3_layer = {'f_fum':n_nodes_hl3,
+                  'weight':tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])),
+                  'bias':tf.Variable(tf.random_normal([n_nodes_hl3]))}
+
+output_layer = {'f_fum':None,
+                'weight':tf.Variable(tf.random_normal([n_nodes_hl3, n_classes])),
+                'bias':tf.Variable(tf.random_normal([n_classes])),}
+
+
+
+def neural_network_model(data):
+    ####INPUT LAYER (HIDDEN LAYER 1)
+    l1 = tf.add(tf.matmul(data,hidden_1_layer['weight']), hidden_1_layer['bias'])
+    l1 = tf.nn.relu(l1)
+    ####HIDDEN LAYER 2
+    l2 = tf.add(tf.matmul(l1,hidden_2_layer['weight']), hidden_2_layer['bias'])
+    l2 = tf.nn.relu(l2)
+    ####HIDDEN LAYER 3
+    l3 = tf.add(tf.matmul(l2,hidden_3_layer['weight']), hidden_3_layer['bias'])
+    l3 = tf.nn.relu(l3)
+    ####OUTPUT LAYER
+    output = tf.matmul(l3,output_layer['weight']) + output_layer['bias']
+    return output
+
+
+
+
+
+
+#
+def predictmood(input_midi_file):
+    prediction = neural_network_model(x)
+    # with open('musicModel.pickle','rb') as f:
+    #     lexicon = pickle.load(f)
+    with tf.Session() as sess:
+        sess.run(tf.global_variables_initializer())
+        saver = tf.train.import_meta_graph(saveFile+'.meta')
+        saver.restore(sess, saveFile)
+        #### CONVERT THE MIDI TO NOTES AND FEATURES (without [0,1])
+        #### need it in the [0 112 1 1 0 0 0 ....] format
+        mid = input_midi_file
+        notes = []
+        time = float(0)
+        prev = float(0)
+        for msg in mid:
+            if time >= 10:
+                break
+            ### this time is in seconds, not ticks
+            time += msg.time
+            if not msg.is_meta:
+                ### only interested in piano channel
+                if msg.channel == 0:
+                    if msg.type == 'note_on':
+                        # note in vector form to train on
+                        note = msg.bytes()
+                        # only interested in the note #and velocity. note message is in the form of [type, note, velocity]
+                        note = note[1] #:3]
+                        # note.append(time - prev)
+                        prev = time
+                        notes.append(note)
+        noteCount = np.zeros(pianoSize)
+        for note in notes:
+            noteCount[note] += 1
+        noteCount = list(noteCount)
+        #features = np.array(list(features))
+        # pos: [1,0] , argmax: 0
+        # neg: [0,1] , argmax: 1
+        result = (sess.run(tf.argmax(prediction.eval(feed_dict={x:[noteCount]}),1)))
+        if result[0] == 0:
+            return ("Sad")
+        elif result[0] == 1:
+            return ("Happy")
+        # with open('mood.txt', 'w') as outfile:
+        #     mood_dict = dict()
+        #     if result[0] == 0:
+        #         mood_dict = {'Mood': "Happy"}
+        #     elif result[0] == 1:
+        #         mood_dict = {'Mood': "Sad"}
+        #     json.dump(mood_dict, outfile)
+#    output.seek(0) #resets the pointer to the data of the file to the start
+#    return output

server/usingMusicNN.py

@@ -1,13 +1,15 @@
 '''
-Thomas Matlak Avi Vajpeyi, Avery Rapson
+    Thomas Matlak Avi Vajpeyi, Avery Rapson
-CS 310 Final Project
+    CS 310 Final Project
-
+    
-Takes example midi file and prints if its happy or sad
+    Loads the NN saved in the dir 'savedFile'. The function predictmood(input_midi_file)
-
+    takes a midi files in MIDO format and returns if it is happy or sad
-Usage:
+    
-python [/path/to/midi/file.mid]
+    Usage:
+    python usingMusicNN.py
 '''
 
+
 import tensorflow as tf
 import json
 from mido import MidiFile