add docstrings, fix choose_by_prob

project/generate.py

@@ -1,42 +1,74 @@
+#!python3
 #!/usr/bin/env python3
+''' This module generates a sample, and create a midi file.
+
+Usage:
+    >>> ./generate.py [trained_model_path] [output_path]
+
+'''
+import settings
+import sys
+import random
+import pickle
 
 import numpy as np
 import tensorflow as tf
 import pypianoroll as roll
+import matplotlib.pyplot as plt
+from tqdm import trange, tqdm
+from music21 import converter, instrument, note, chord, stream
 from keras.layers import Input, Dense, Conv2D
 from keras.models import Model
-from tensorflow.keras import layers
 from keras.layers import Input, Dense, Conv2D, Flatten, LSTM, Dropout, TimeDistributed, RepeatVector
 from keras.models import Model, Sequential
-import matplotlib.pyplot as plt
-import settings
-import random
-import pickle
-from tqdm import trange, tqdm
-import sys
-from music21 import converter, instrument, note, chord, stream
+
+
+def choose_by_prob(list_of_probs):
+    ''' This functions a list of values and assumed
+    that if the value is bigger it should by returned often
+    
+    It was crated to give more options to choose than argmax function,
+    thus is more than one way that you can develop a melody.
+    
+    Returns a index of choosen value from given list.
+    '''
+    sum_prob = np.array(list_of_probs).sum()
+    prob_normalized = [x/sum_prob for x in list_of_probs]
+    cumsum = np.array(prob_normalized).cumsum()
+    prob_cum = cumsum.tolist()
+    random_x = random.random()
+    for i, x in enumerate(prob_cum):
+        if random_x < x:
+            return i
 
 trained_model_path = sys.argv[1]
 output_path = sys.argv[2]
 
-# load and predict
+# load model and dictionary that can translate back index_numbers to notes
+# this dictionary is generated with model
 print('Loading... {}'.format(trained_model_path))
 model = pickle.load(open(trained_model_path, 'rb'))
 int_to_note = pickle.load(open('{}_dict'.format(trained_model_path), 'rb'))
 
-seed = [random.randint(0,50) for x in range(8)]
+# TODO: 16 it should a variable by integrated with model seq_len
+# TODO: random.randint(0,50), the range should be a variable of lenght of vocab size
+seed = [random.randint(0,250) for x in range(16)] 
 
 music = []
 
 print('Generating...')
-for i in trange(500):
-    predicted_vector = model.predict(np.array(seed).reshape(1,8,1))
-    predicted_index = np.argmax(predicted_vector)
-
+for i in trange(124):
+    #TODO: 16 it should a variable by integrated with model seq_len
+    predicted_vector = model.predict(np.array(seed).reshape(1,16,1))
+    # using best fitted note
+    # predicted_index = np.argmax(predicted_vector)
+    # using propability distribution for choosing note
+    # to prevent looping
+    predicted_index = choose_by_prob(predicted_vector)
     music.append(int_to_note[predicted_index])
-
     seed.append(predicted_index)
-    seed = seed[1:9]
+    #TODO: 16 it should a variable by integrated with model seq_len
+    seed = seed[1:1+16]
 
 
 print('Saving...')

project/midi.py

@@ -1,5 +1,17 @@
+#!python3
 #!/usr/bin/env python3
 
+''' This module contains functions to endocing midi files into data samples
+that is prepared for model training.
+
+    midi_folder_path - the path to directiory containing midi files
+    output_path - the output path where will be created samples of data
+
+    Usage:
+        >>> ./midi.py <midi_folder_path> <output_path>
+
+'''
+
 import settings
 import pypianoroll as roll
 import numpy as np
@@ -14,8 +26,22 @@ import music21
 
 midi_folder_path = sys.argv[1]
 output_path = sys.argv[2]
+seq_len = int(sys.argv[3])
 
-def to_sequence(midi_path):
+def to_sequence(midi_path, seq_len):
+    ''' This function is supposed to be used on one midi file in directory loop.
+    Its encoding midi files, into sequances of given lenth as a train_X,
+    and the next note as a train_y. Also splitting midi samples into 
+    instrument group.
+
+    Use for LSTM neural network.
+        
+        Parameters:
+            - midi_path: path to midi file
+            - seq_len: lenght of sequance before prediction
+            
+        Returns: Tuple of train_X, train_y directories'''
+        
     seq_by_instrument = defaultdict( lambda : [] )
     midi_file = music21.converter.parse(midi_path)
     stream = music21.instrument.partitionByInstrument(midi_file)
@@ -36,9 +62,9 @@ def to_sequence(midi_path):
     y_train_by_instrument = defaultdict( lambda : [] )
 
     for instrument, sequence in seq_by_instrument.items():
-        for i in range(len(sequence)-8) :
-            X_train_by_instrument[instrument].append(np.array(sequence[i: i + 8])) # <seq lenth
-            y_train_by_instrument[instrument].append(np.array(sequence[i + 8]))
+        for i in range(len(sequence)-(seq_len)) :
+            X_train_by_instrument[instrument].append(np.array(sequence[i:i+seq_len])) # <seq lenth
+            y_train_by_instrument[instrument].append(np.array(sequence[i+seq_len]))
             # TODO: Notes to integers
 
     return X_train_by_instrument, y_train_by_instrument
@@ -52,7 +78,13 @@ def main():
     for directory, subdirectories, files in os.walk(midi_folder_path):
         for midi_file in tqdm(files):
             midi_file_path = os.path.join(directory, midi_file)
-            _X_train, _y_train = to_sequence(midi_file_path)
+            
+            # some midi files can be corupted, and cannot be parsed
+            # so we just omit corupted files, and go to the next file.
+            try:
+                _X_train, _y_train = to_sequence(midi_file_path, seq_len)
+            except music21.midi.MidiException:
+                continue
 
             for (X_key, X_value), (y_key, y_value) in zip(_X_train.items(), _y_train.items()):
                 train_X[X_key].extend(np.array(X_value))

project/train.py

@@ -1,8 +1,9 @@
+#!python3
 #!/usr/bin/env python3
 
 import tensorflow as tf
 import settings
-from tensorflow.keras import layers
+#from tensorflow.keras import layers
 from keras.layers import Input, Dense, Conv2D, Flatten, LSTM, Dropout, TimeDistributed, RepeatVector, Activation, Bidirectional, Reshape
 from keras.models import Model, Sequential
 from keras.utils.np_utils import to_categorical
@@ -18,7 +19,9 @@ def load_data(samples_path):
 
 # TODO: make transformer class with fit, transform and reverse definitions
 def preprocess_samples(train_X, train_y):
-    vocab = np.unique(train_X)
+    vocab_X = np.unique(train_X)
+    vocab_y = np.unique(train_y)
+    vocab = np.concatenate([vocab_X, vocab_y])
     n_vocab = vocab.shape[0]
     note_to_int = dict((note, number) for number, note in enumerate(vocab))
     int_to_note = dict((number, note) for number, note in enumerate(vocab))