Merge branch 'seq2seq_model' of s444337/praca-magisterska into master

seq2seq model

project/extract.py

@@ -0,0 +1,74 @@
+import os
+import sys
+import argparse
+import pickle
+
+from midi_processing import extract_data, analyze_data
+
+def make_folder_if_not_exist(path):
+    try:
+        os.mkdir(path)
+    except:
+        pass
+
+def parse_argv():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('midi_pack', help='folder name for midi pack in midi_packs folder', type=str)
+    parser.add_argument('--n', help='name for experiment', type=str)
+    parser.add_argument('--b', help='lengh of sequence in bars', type=int)
+    parser.add_argument('-a', help='analize data', action='store_true')
+    args = parser.parse_args()
+    return args
+
+def ask_for_workflow():
+    '''MODEL WORKFLOW DIALOG'''
+    number_of_instruments = int(input('Please specify number of instruments\n'))
+    model_workflow = dict()
+    for i in range(number_of_instruments):
+        input_string = input('Please specify a workflow step <Instrument> [<Second Instrument>] <mode> {m : melody, a : arrangment}\n')
+        tokens = input_string.split()
+        if tokens[-1] == 'm':
+            model_workflow[i] = (tokens[0], 'melody')
+        elif tokens[-1] == 'a':
+            model_workflow[i] = ((tokens[1], tokens[0]), 'arrangment')
+        else:
+            raise ValueError("The step definitiom must end with {'m', 'a'}");
+    
+    make_folder_if_not_exist(os.path.join('training_sets', EXPERIMENT_NAME))
+    pickle.dump(model_workflow, open(os.path.join('training_sets', EXPERIMENT_NAME, 'workflow.pkl'),'wb'))
+    
+    return model_workflow
+
+def extract_from_folder(model_workflow):
+    for key, (instrument, how) in model_workflow.items():
+        if how == 'melody':
+            instrument_name = instrument
+        else:
+            instrument_name = instrument[1]
+    
+        make_folder_if_not_exist(os.path.join('training_sets', EXPERIMENT_NAME))
+        save_path = os.path.join('training_sets', EXPERIMENT_NAME, instrument_name.lower() + '_data.pkl')
+        
+        x_train, y_train, program = extract_data(midi_folder_path=os.path.join('midi_packs', MIDI_PACK_NAME),
+                                                 how=how, 
+                                                 instrument=instrument,
+                                                 bar_in_seq=BARS_IN_SEQ)
+        
+        pickle.dump((x_train, y_train, program), open(save_path,'wb'))
+
+if __name__ == '__main__':
+    args = parse_argv()
+
+    MIDI_PACK_NAME = args.midi_pack
+    EXPERIMENT_NAME = args.n
+    BARS_IN_SEQ = args.b
+    if not EXPERIMENT_NAME:
+        EXPERIMENT_NAME = MIDI_PACK_NAME
+    if not BARS_IN_SEQ:
+        BARS_IN_SEQ = 4
+    ANALIZE = args.a
+
+    if ANALIZE:
+        analyze_data(os.path.join('midi_packs', MIDI_PACK_NAME))
+    else:
+        extract_from_folder(ask_for_workflow())

project/generate.py

@@ -1,96 +1,94 @@
-#!python3
+from midi_processing import MultiTrack, SingleTrack, Stream
-#!/usr/bin/env python3
+from model import Seq2SeqModel, seq_to_numpy
-''' This module generates a sample, and create a midi file.
+from tqdm import tqdm
-
+import argparse
-Usage:
+import os
-    >>> ./generate.py [trained_model_path] [output_path]
-
-'''
-import settings
-import sys
-import random
 import pickle
 
-import numpy as np
+parser = argparse.ArgumentParser()
-import tensorflow as tf
+parser.add_argument('n', help='name for experiment', type=str)
-import pypianoroll as roll
+parser.add_argument('s', help='session name', type=str)
-import matplotlib.pyplot as plt
+parser.add_argument('--i', help='number of midis to generate', type=int)
-from tqdm import trange, tqdm
+parser.add_argument('--l', help='latent_dim_of_model', type=int)
-from music21 import converter, instrument, note, chord, stream
+parser.add_argument('--m', help="mode {'from_seq', 'from_state}'", type=str)
-from keras.layers import Input, Dense, Conv2D
+args = parser.parse_args()
-from keras.models import Model
-from keras.layers import Input, Dense, Conv2D, Flatten, LSTM, Dropout, TimeDistributed, RepeatVector
-from keras.models import Model, Sequential
 
+EXPERIMENT_NAME = args.n
+SESSION_NAME = args.s
+GENERETIONS_COUNT = args.i
+LATENT_DIM = args.l
+MODE = args.m
 
-def choose_by_prob(list_of_probs):
+if not GENERETIONS_COUNT:
-    ''' This functions a list of values and assumed
+    GENERETIONS_COUNT = 1
-    that if the value is bigger it should by returned often
 
-    It was crated to give more options to choose than argmax function,
+if not LATENT_DIM:
-    thus is more than one way that you can develop a melody.
+    LATENT_DIM = 256
     
-    Returns a index of choosen value from given list.
+if not MODE:
-    '''
+    MODE = 'from_seq'
-    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]
+model_workflow = pickle.load(open(os.path.join('training_sets', EXPERIMENT_NAME, 'workflow.pkl'),'rb'))
-output_path = sys.argv[2]
 
-# load model and dictionary that can translate back index_numbers to notes
+band = dict()
-# this dictionary is generated with model
+for key, value in model_workflow.items():
-print('Loading... {}'.format(trained_model_path))
+    if isinstance(value[0], str):
-model = pickle.load(open(trained_model_path, 'rb'))
+        instrument = value[0]
-int_to_note, n_vocab, seq_len = pickle.load(open('{}_dict'.format(trained_model_path), 'rb'))
+        generator = None
-
-seed = [random.randint(0,n_vocab) for x in range(seq_len)] 
-music = []
-
-print('Generating...')
-for i in trange(124):
-    predicted_vector = model.predict(np.array(seed).reshape(1,seq_len,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[1:1+seq_len]
-
-
-print('Saving...')
-offset = 0
-output_notes = []
-for _event in tqdm(music):
-    event, note_len = _event.split(';')
-    if (' ' in event) or event.isdigit():
-        notes_in_chord = event.split(' ')
-        notes = []
-        for current_note in notes_in_chord:
-            new_note = note.Note(current_note)
-            new_note.storedInstrument = instrument.Piano()
-            notes.append(new_note)
-        new_chord = chord.Chord(notes)
-        new_chord.offset = offset
-        output_notes.append(new_chord)
     else:
-        new_note = note.Note(event)
+        instrument = value[0][1]
-        new_note.offset = offset
+        generator = value[0][0]
-        new_note.storedInstrument = instrument.Piano()
-        output_notes.append(new_note)
             
-    offset += float(note_len)
+    band[instrument] = [None, None, generator]
 
-midi_stream = stream.Stream(output_notes)
+'''LOAD MODELS'''
+for instrument in tqdm(band):
     
-midi_stream.write('midi', fp='{}.mid'.format(output_path))
+    data_path = os.path.join('training_sets', EXPERIMENT_NAME, instrument.lower() + '_data.pkl')
+    model_path = os.path.join('models', EXPERIMENT_NAME, instrument.lower() + '_model.h5')
     
-print('Done!')
+    x_train, y_train, program = pickle.load(open(data_path,'rb'))
+    model = Seq2SeqModel(LATENT_DIM, x_train, y_train)
+    model.load(model_path)
+    band[instrument][0] = model
+    band[instrument][1] = program
+
+for midi_counter in tqdm(range(GENERETIONS_COUNT)):
+    ''' MAKE MULTIINSTRUMENTAL MUSIC !!!'''
+    notes = dict()
+
+    for instrument, (model, program, generator) in band.items():
+        if generator == None:
+            notes[instrument] = model.develop(mode=MODE)
+        else:
+            input_data = seq_to_numpy(notes[generator],
+                            model.transformer.x_max_seq_length,
+                            model.transformer.x_vocab_size,
+                            model.transformer.x_transform_dict)
+            notes[instrument] = model.predict(input_data)[:-1]
+
+    '''COMPILE TO MIDI'''
+    generated_midi = MultiTrack()
+    for instrument, (model, program, generator) in band.items():
+        if instrument == 'Drums':
+            is_drums = True
+        else:
+            is_drums = False
+
+        stream = Stream(first_tick=0, notes=notes[instrument])
+        track = SingleTrack(name=instrument ,program=program, is_drum=is_drums, stream=stream)
+        generated_midi.tracks.append(track)
+
+    # make folder for new experiment
+    try:
+        os.mkdir(os.path.join('generated_music', EXPERIMENT_NAME))
+    except:
+        pass
+    try:
+        os.mkdir(os.path.join('generated_music', EXPERIMENT_NAME, SESSION_NAME))
+    except:
+        pass
+
+    save_path = os.path.join('generated_music', EXPERIMENT_NAME, SESSION_NAME, f'{EXPERIMENT_NAME}_{midi_counter}_{MODE}_{LATENT_DIM}.mid')
+    generated_midi.save(save_path)
+    # print(f'Generated succefuly to {save_path}')

project/midi.py

@@ -1,133 +0,0 @@
-#!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> <sequence_lenth>
-'''
-
-import settings
-import pypianoroll as roll
-import numpy as np
-import os
-from tqdm import tqdm
-from math import floor
-import sys
-from collections import defaultdict
-import pickle
-from music21 import converter, instrument, note, chord, stream
-import music21
-
-class MidiParseError(Exception):
-   """Error that is raised then midi file cannot be parsed"""
-   pass
-
-
-def parse_argv(argv):
-    '''This function is parsing given arguments when running a midi script.
-    Returns a tuple consinting of  midi_folder_path, output_path, seq_len'''
-    try:
-        midi_folder_path = argv[1]
-        output_path = argv[2]
-        seq_len = int(argv[3])
-        return midi_folder_path, output_path, seq_len
-    except IndexError:
-        raise AttributeError('You propably didnt pass parameters to run midi.py script.\
-                             >>> ./midi.py <midi_folder_path> <output_path> <sequence_lenth>')
-
-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 dictionaries consisinting of samples of song grouped by instruments
-    '''
-        
-    seq_by_instrument = defaultdict( lambda : [] )
-    
-    try:
-        midi_file = music21.converter.parse(midi_path)
-    except music21.midi.MidiException:
-        raise MidiParseError
-    stream = music21.instrument.partitionByInstrument(midi_file)
-    for part in stream:
-        for event in part:
-            if part.partName != None:
-                if isinstance(event, music21.note.Note):
-                    to_export_event = '{};{}'.format(str(event.pitch), float(event.quarterLength))
-                    seq_by_instrument[part.partName].append(to_export_event)
-                elif isinstance(event, music21.chord.Chord):
-                    to_export_event = '{};{}'.format(' '.join(str(note) for note in event.pitches), float(event.quarterLength))
-                    seq_by_instrument[part.partName].append(to_export_event)
-
-    X_train_by_instrument = defaultdict( lambda : [] )
-    y_train_by_instrument = defaultdict( lambda : [] )
-
-    for instrument, sequence in seq_by_instrument.items():
-        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]))
-
-    return X_train_by_instrument, y_train_by_instrument
-
-def colect_samples(midi_folder_path, seq_len):
-    '''This function is looping throuth given directories and
-    collecting samples from midi files.
-    
-        Parameters: midi_folder_path - a path to directory with midi files
-                    seq_len - a lenth of train_X sample that tells 
-                    how many notes is given do LSTM to predict the next note.
-        
-        Returns: Tuple of train_X, train_y dictionaries consisinting
-        of samples of all songs in directory grouped by instruments.
-    '''
-    
-    print('Collecting samples...')
-    train_X = defaultdict( lambda : [] )
-    train_y = defaultdict( lambda : [] )
-
-    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)
-            try:
-                _X_train, _y_train = to_sequence(midi_file_path, seq_len)
-            except MidiParseError:
-                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))
-                train_y[y_key].extend(np.array(y_value))
-
-    return train_X, train_y
-
-def save_samples(output_path, samples):
-    '''This function save samples to npz packages, splitted by instrument.'''
-    
-    print('Saving...')
-    
-    if not os.path.exists(output_path):
-        os.makedirs(output_path)
-        
-    train_X, train_y = samples
-    for (X_key, X_value), (y_key, y_value) in tqdm(zip(train_X.items(), train_y.items())):
-        if X_key == y_key:
-            np.savez_compressed('{}/{}.npz'.format(output_path, X_key), np.array(X_value), np.array(y_value))
-
-def main():
-    midi_folder_path, output_path, seq_len = parse_argv(sys.argv)
-    save_samples(output_path, colect_samples(midi_folder_path, seq_len))
-    print('Done!')
-
-if __name__ == '__main__':
-    main()

project/midi_processing.py

@@ -1,324 +1,16 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[1]:
-
-
 import os
+import sys
 import pickle
+import operator
 import numpy as np
 from collections import defaultdict
+from collections import Counter 
+from random import randint
 
 import pretty_midi as pm
 from tqdm import tqdm
 
-
+# TODO: Stream class is no logner needed <- remore from code and make just SingleTrack.notes instead on SingleTrack.stream.notes
-# In[98]:
-
-
-TODO = '''
-TODO: put methods of data extraction for seq2seq arangment model to multitrack class [DONE]
-TODO: make functions for data extraction for seq2seq model for riff/melody generation [DONE]
-'''
-
-
-# In[367]:
-
-
-# '''return a dictionary with tracks indexes grouped by instrument class'''
-# tracks = file.tracks
-# names = [track.name for track in tracks]
-# uniqe_instruemnts = set(names)
-# tracks_by_instrument = dict()
-# for key in uniqe_instruemnts:
-#     tracks_by_instrument[key] = []
-    
-# for i, track in enumerate(tracks):
-#     tracks_by_instrument[track.name].append(i)
-    
-# tracks_by_instrument
-
-
-# In[368]:
-
-
-# def get_posible_pairs(instrument_x, instrument_y):
-#     '''it takes two lists, and return a list of tuples with every posible 2-element combination
-#     parameters:
-#     -----------
-#         instrument_x, instrument_y : string {'Guitar','Bass','Drums'} 
-#             a string that represent a instrument class you want to look for in midi file.
-    
-#     returns:
-#     ----------
-#         pairs: list of tuples
-#             a list of posible 2-element combination of two lists
-#     '''
-#     x_indexes = tracks_by_instrument[instrument_x]
-#     y_indexes = tracks_by_instrument[instrument_y]
-#     pairs = []
-# #     pairs = [(x,y) for x in x_indexes for y in y_indexes]
-
-#     for x in x_indexes:
-#         for y in y_indexes:
-#             pairs.append((x,y)) 
-
-#     return pairs
-
-
-# In[369]:
-
-
-#  def get_common_bars_for_every_possible_pair(pairs)
-#     ''' for every possible pair of given instrument classes
-#     returns common bars from multitrack'''
-#     x_bars = []
-#     y_bars = []
-#     for x_track_index, y_track_index in pairs:
-#         _x_bars, _y_bars = get_common_bars(file.tracks[x_track_index], file.tracks[y_track_index])
-#         x_bars.extend(_x_bars)
-#         y_bars.extend(_y_bars)
-
-#     return x_bars, y_bars
-
-
-# In[370]:
-
-
-# def get_data_seq2seq_arrangment(self, bars_in_seq):
-#     ## This is the end of extracting data from midis to seq2seq arranging network.
-#     '''this method is returning a sequances of given lenth by rolling this lists of x and y for arrangemt generation'''
-#     x_seq = []
-#     y_seq = []
-
-#     for i in range(len(x_bars) - bars_in_seq + 1):
-#         x_seq_to_add = [note for bar in x_bars[i:i+bars_in_seq] for note in bar ]
-#         y_seq_to_add = [note for bar in y_bars[i:i+bars_in_seq] for note in bar ]
-#         x_seq.append(x_seq_to_add)
-#         y_seq.append(y_seq_to_add)
-
-#     len(x_seq), len(y_seq)
-#     # get_bar_len(y_seq[0])
-
-
-# In[371]:
-
-
-# def get_track_by_instrument(self):
-#     '''return a dictionary with tracks indexes grouped by instrument class'''
-#     tracks = self.tracks
-#     names = [track.name for track in tracks]
-#     uniqe_instruemnts = set(names)
-#     tracks_by_instrument = dict()
-#     for key in uniqe_instruemnts:
-#         tracks_by_instrument[key] = []
-
-#     for i, track in enumerate(tracks):
-#         tracks_by_instrument[track.name].append(i)
-
-#     return tracks_by_instrument
-
-
-# In[372]:
-
-
-# def get_data_seq2seq_melody(self,instrument_class, x_seq_len=4)
-#     '''return a list of bars with content for every track with given instrument class for melody generaiton'''
-
-#     instrument_tracks = tracks_by_instrument[instrument_class]
-
-#     for track_index in instrument_tracks:
-#         # make below as function: get_bars_with_content
-#         bars = file.tracks[track_index].stream_to_bars()
-#         bars_indexes_with_content = get_bar_indexes_with_content(bars)
-#         bars_with_content = [bars[i] for i in get_bar_indexes_with_content(bars)]
-
-#         # make below as function: get_sequances_from_bars (for seq2seq melody generator)
-#         x_seq = []
-#         y_bar = []
-#         for i in range(len(bars_with_content)-seq_len-1):
-#             _x_seq = bars_with_content[i:i+seq_len]
-#             _y_bar = bars_with_content[i+seq_len]
-#             x_seq.append(_x_seq)
-#             y_bar.append(_y_bar)
-
-
-#     len(x_seq), len(y_bar)
-# # print( ' x:' ,x_seq[1],'\n', 'y: ', y_bar[1],'\n', 'seq: ',bars_with_content[1:6])
-
-
-# In[15]:
-
-
-def get_bar_indexes_with_content(bars):
-    '''this method is looking for non-empty bars in the tracks bars
-    the empty bar consist of only rest notes.
-    returns: a set of bars indexes with notes
-    '''
-    bars_indexes_with_content = set()
-    for i, bar in enumerate(bars):
-        if bar_has_content(bar):
-            bars_indexes_with_content.add(i)
-
-    return bars_indexes_with_content    
-
-
-# In[4]:
-
-
-def get_bars_with_content(bars):
-    '''this method is looking for non-empty bars in the tracks bars
-    the empty bar consist of only rest notes.
-    returns: a set of bars with notes
-    '''
-    bars_with_content = []
-    for bar in bars:
-        if bar_has_content(bar):
-            bars_with_content.append(bar)
-
-    return bars_with_content  
-
-
-# In[5]:
-
-
-def get_common_bars(track_x,track_y):
-    '''return common bars, for two tracks is song
-    return X_train, y_train list of 
-    '''
-    bars_x = track_x.stream_to_bars()
-    bars_y = track_y.stream_to_bars()
-    bwc_x = get_bar_indexes_with_content(bars_x)
-    bwc_y = get_bar_indexes_with_content(bars_y)
-
-    common_bars = bwc_x.intersection(bwc_y)
-    common_bars_x = [bars_x[i] for i in common_bars]
-    common_bars_y = [bars_y[i] for i in common_bars]
-    return common_bars_x, common_bars_y
-
-
-# In[6]:
-
-
-def get_bar_len(bar):
-    """calculate a lenth of a bar
-    parameters:
-        bar : list
-            list of "notes", tuples like (pitches, len)
-    """
-    time = 0
-    for note in bar:
-        time += note[1]
-    return time
-
-
-# In[7]:
-
-
-def bar_has_content(bar):
-    '''check if bar has any musical information, more accurate
-    it checks if in a bar is any non-rest event like note, or chord
-    
-    parameters:
-    -----------
-        bar: list
-            list of notes
-            
-    return:
-    -------
-        bool:
-            True if bas has concent and False of doesn't
-    '''
-    bar_notes = len(bar)
-    count_rest = 0
-    for note in bar:
-        if note[0] == (-1,):
-            count_rest += 1
-    if count_rest == bar_notes:
-        return False
-    else:
-        return True
-
-
-# In[8]:
-
-
-def round_to_sixteenth_note(x, base=0.25):
-        '''round value to closest multiplication by base
-        in default to 0.25 witch is sisteenth note accuracy 
-        '''
-        
-        return base * round(x/base)
-
-
-# In[9]:
-
-
-def parse_pretty_midi_instrument(instrument, resolution, time_to_tick, key_offset):
-    ''' arguments: a prettyMidi instrument object
-        return: a custom SingleTrack object
-    '''
-    
-    first_tick = None
-    prev_tick = 0
-    prev_note_lenth = 0
-    max_rest_len = 4.0
-
-    notes = defaultdict(lambda:[set(), set()])
-    for note in instrument.notes:
-        if first_tick == None:
-#             first_tick = round_to_sixteenth_note(time_to_tick(note.start)/resolution)
-            first_tick = 0
-            
-        tick = round_to_sixteenth_note(time_to_tick(note.start)/resolution)
-        # add rest if needed
-        if prev_tick != None:
-            act_tick = prev_tick + prev_note_lenth
-            if act_tick < tick:
-                rest_lenth = tick - act_tick
-                while rest_lenth > max_rest_len:
-                    notes[act_tick] = [{-1},{max_rest_len}]
-                    act_tick += max_rest_len
-                    rest_lenth -= max_rest_len
-                notes[act_tick] = [{-1},{rest_lenth}]
-
-        note_lenth = round_to_sixteenth_note(time_to_tick(note.end-note.start)/resolution)
-        
-        if -1 in notes[tick][0]:
-            notes[tick] = [set(), set()]
-        
-        if instrument.is_drum:
-            notes[tick][0].add(note.pitch)
-        else:
-            notes[tick][0].add(note.pitch+key_offset)
-        notes[tick][1].add(note_lenth)
-
-        prev_tick = tick
-        prev_note_lenth = note_lenth
-    
-    notes = [(tuple(e[0]), max(e[1])) for e in notes.values()]
-
-    name = 'Drums' if instrument.is_drum else pm.program_to_instrument_class(instrument.program)
-    return SingleTrack(name, instrument.program, instrument.is_drum, Stream(first_tick,notes) )
-
-
-# In[10]:
-
-
-def remove_duplicated_sequences(xy_tuple):
-    x = xy_tuple[0]
-    y = xy_tuple[1]
-    x_freeze = [tuple(seq) for seq in x]
-    y_freeze = [tuple(seq) for seq in y]
-    unique_data = list(set(zip(x_freeze,y_freeze)))
-    x_unique = [seq[0] for seq in unique_data]
-    y_unique = [seq[1] for seq in unique_data]
-    return x_unique, y_unique
-
-
-# In[11]:
-
-
 class Stream():
     
     def __init__ (self, first_tick, notes):
@@ -328,10 +20,6 @@ class Stream():
     def __repr__(self):
         return '<Stream object with {} musical events>'.format(len(self.notes))
       
-
-# In[12]:
-
-
 class SingleTrack():
     '''class of single track in midi file encoded from pretty midi library
     
@@ -352,6 +40,7 @@ class SingleTrack():
         self.program = program
         self.is_drum = is_drum
         self.stream = stream
+        self.is_melody = self.check_if_melody()
         
     def __repr__(self):
         return "<SingleTrack object. Name:{}, Program:{}, is_drum:{}>".format(self.name, self.program, self.is_drum)
@@ -387,13 +76,21 @@ class SingleTrack():
     def stream_to_bars(self, beat_per_bar=4):
         '''it takes notes and split it into equaly time distibuted sequances
         if note is between bars, the note is splited into two notes, with time sum equal to the note between bars.
+        
         arguments:
+        ----------
             stream: list of "notes"
+            
         return:
+        -------
             bars: list: list of lists of notes, every list has equal time. in musical context it returns bars
         '''
         # TODO: if last bar of sequance has less notes to has time equal given bar lenth it is left shorter
         # fill the rest of bar with rests
+        
+        # FIXME: there is a problem, where note is longer that bar and negative time occured
+        # split note to max_rest_note, the problem occured when note is longer then 2 bars
+        
         notes = self.stream.notes
         bars = []
         time = 0
@@ -408,8 +105,15 @@ class SingleTrack():
                 bars.append([])
                 
             if add_tail:
-                bars[bar_index].append(tail_note)
+                tail_pitch = note_pitch(tail_note)
-                time += note_len(tail_note)
+                while tail_note_len > beat_per_bar:
+                    bars[bar_index].append((tail_pitch, beat_per_bar))
+                    tail_note_len -= beat_per_bar
+                    bar_index += 1
+                    bars.append([])
+                       
+                bars[bar_index].append((tail_pitch, tail_note_len))
+                time += tail_note_len
                 add_tail = False
 
             time += note_len(note)
@@ -436,8 +140,41 @@ class SingleTrack():
                 
         return bars
     
+    def check_if_melody(self):
+        '''checks if Track object could be a melody
         
-# In[99]:
+            it checks if percentage of single notes in Track.stream.notes is higher than treshold of 90%
+            TODO: and there is at least 3 notes in bar per average
+        
+        '''
+        events = None
+        single_notes = None
+        content_lenth = None
+        
+        for note in self.stream.notes:
+            if self.name not in ['Bass','Drums']:
+                events = 0
+                content_lenth = 0
+                single_notes = 0
+                if note[0][0] != -1: # if note is not a rest
+                    events += 1
+                    content_lenth += note[1]
+                    if len(note[0]) == 1: # if note is a single note, not a chord
+                        single_notes += 1
+
+        if events != None:
+            if events == 0 or content_lenth == 0:
+                return False
+            else:
+                single_notes_rate = single_notes/events
+                density_rate = events/content_lenth
+                if single_notes_rate >= 0.9 and density_rate < 2:
+                    self.name = 'Melody'
+                    return True
+                else:
+                    return False
+        else:
+            return False
           
           
 class MultiTrack():
@@ -452,13 +189,14 @@ class MultiTrack():
     
     def __init__(self, path=None, tempo=100):
         self.tempo = tempo
-        self.pm_obj = pm.PrettyMIDI(path, initial_tempo=self.tempo)
+        self.pm_obj = pm.PrettyMIDI(path, initial_tempo=self.tempo) # changename to self.PrettyMIDI
         self.res = self.pm_obj.resolution
         self.time_to_tick = self.pm_obj.time_to_tick
         self.name = path
         self.tracks = [parse_pretty_midi_instrument(instrument, self.res, self.time_to_tick, self.get_pitch_offset_to_C() ) for instrument in self.pm_obj.instruments]  
         self.tracks_by_instrument = self.get_track_by_instrument()
     
+    # TODO: this function is deprecated <- remove from code
     def get_multiseq(self):
         '''tracks: list of SingleTrack objects
         reaturn a dictionary of sequences for every sequence in SingleTrack
@@ -476,6 +214,14 @@ class MultiTrack():
 
         return multiseq
       
+    def get_programs(self, instrument):
+        program_list = []
+        for track in self.tracks:
+          if track.name == instrument:
+            program_list.append(track.program)
+            
+        return program_list
+    
     def get_pitch_offset_to_C(self):
         '''to get better train resoult without augmenting midis to all posible keys
         we assumed that most frequent note is the rootnote of song then calculate
@@ -526,7 +272,10 @@ class MultiTrack():
         return x_bars, y_bars
     
     def get_data_seq2seq_arrangment(self, x_instrument, y_instrument, bars_in_seq=4):
-        '''this method is returning a sequances of given lenth by rolling this lists of x and y for arrangemt generation'''
+        '''this method is returning a sequances of given lenth by rolling this lists of x and y for arrangemt generation
+        x and y has the same bar lenth, and represent the same musical phrase playd my difrent instruments (tracks) 
+        
+        '''
         x_seq = []
         y_seq = []
         x_bars, y_bars = self.get_common_bars_for_every_possible_pair(x_instrument, y_instrument)
@@ -540,7 +289,12 @@ class MultiTrack():
         return x_seq, y_seq
     
     def get_data_seq2seq_melody(self,instrument_class, x_seq_len=4):
-        '''return a list of bars with content for every track with given instrument class for melody generaiton'''
+        '''return a list of bars with content for every track with given instrument class for melody generaiton
+        x_seq_len and y_seq_len
+        
+        x previous sentence, y next sentence of the same melody line
+        
+        '''
 
         instrument_tracks = self.tracks_by_instrument[instrument_class]
 
@@ -573,13 +327,7 @@ class MultiTrack():
         '''
         x_indexes = self.tracks_by_instrument[instrument_x]
         y_indexes = self.tracks_by_instrument[instrument_y]
-#         pairs = []
         pairs = [(x,y) for x in x_indexes for y in y_indexes]
-
-#         for x in x_indexes:
-#             for y in y_indexes:
-#                 pairs.append((x,y)) 
-
         return pairs
     
     def show_map(self):
@@ -597,11 +345,215 @@ class MultiTrack():
             print(track.name[:4],':', track_str)
 
             
+def stream_to_bars(notes, beat_per_bar=4):
+        '''it takes notes and split it into equaly time distibuted sequances
+        if note is between bars, the note is splited into two notes, with time sum equal to the note between bars.
+        arguments:
+            stream: list of "notes"
+        return:
+            bars: list: list of lists of notes, every list has equal time. in musical context it returns bars
+        '''
+        # TODO: if last bar of sequance has less notes to has time equal given bar lenth it is left shorter
+        # fill the rest of bar with rests
         
-# In[104]:
+        # FIXME: there is a problem, where note is longer that bar and negative time occured
+        # split note to max_rest_note, the problem occured when note is longer then 2 bars - FIXED
+        
+        bars = []
+        time = 0
+        bar_index = 0
+        add_tail = False
+        note_pitch = lambda note: note[0]
+        note_len = lambda note: note[1]
+        for note in notes:
+            try:
+                temp = bars[bar_index]
+            except IndexError:
+                bars.append([])
+                
+            if add_tail:
+                tail_pitch = note_pitch(tail_note)
+                while tail_note_len > beat_per_bar:
+                    bars[bar_index].append((tail_pitch, beat_per_bar))
+                    tail_note_len -= beat_per_bar
+                    bar_index += 1
+                       
+                bars[bar_index].append((tail_pitch, tail_note_len))
+                time += tail_note_len
+                add_tail = False
+            time += note_len(note)
+
+            if time == beat_per_bar:
+                bars[bar_index].append(note)
+                time = 0
+                bar_index += 1
+
+            elif time > beat_per_bar: # if note is between bars
+                between_bars_note_len =  note_len(note)
+                tail_note_len = time - beat_per_bar
+                leading_note_len = between_bars_note_len - tail_note_len
+                leading_note = (note_pitch(note), leading_note_len)
+                bars[bar_index].append(leading_note)
+                tail_note = (note_pitch(note), tail_note_len)
+
+                add_tail = True
+                time = 0
+                bar_index += 1
+            else:
+                bars[bar_index].append(note)
+                
+        return bars
+
+def get_bar_len(bar):
+    """calculate a lenth of a bar
+    parameters:
+        bar : list
+            list of "notes", tuples like (pitches, len)
+    """
+    time = 0
+    for note in bar:
+        time += note[1]
+    return time
+  
+def get_common_bars(track_x,track_y):
+    '''return common bars, for two tracks is song
+    return X_train, y_train list of 
+    '''
+    bars_x = track_x.stream_to_bars()
+    bars_y = track_y.stream_to_bars()
+    bwc_x = get_bar_indexes_with_content(bars_x)
+    bwc_y = get_bar_indexes_with_content(bars_y)
+
+    common_bars = bwc_x.intersection(bwc_y)
+    common_bars_x = [bars_x[i] for i in common_bars]
+    common_bars_y = [bars_y[i] for i in common_bars]
+    return common_bars_x, common_bars_y
+      
+def get_bar_indexes_with_content(bars):
+    '''this method is looking for non-empty bars in the tracks bars
+    the empty bar consist of only rest notes.
+    returns: a set of bars indexes with notes
+    '''
+    bars_indexes_with_content = set()
+    for i, bar in enumerate(bars):
+        if bar_has_content(bar):
+            bars_indexes_with_content.add(i)
+
+    return bars_indexes_with_content   
+
+def get_bars_with_content(bars):
+    '''this method is looking for non-empty bars in the tracks bars
+    the empty bar consist of only rest notes.
+    returns: a set of bars with notes
+    '''
+    bars_with_content = []
+    for bar in bars:
+        if bar_has_content(bar):
+            bars_with_content.append(bar)
+
+    return bars_with_content  
   
   
-def extract_data(midi_folder_path=None, how=None, instrument=None, remove_duplicates=True):
+def bar_has_content(bar):
+    '''check if bar has any musical information, more accurate
+    it checks if in a bar is any non-rest event like note, or chord
+    
+    parameters:
+    -----------
+        bar: list
+            list of notes
+            
+    return:
+    -------
+        bool:
+            True if bas has concent and False of doesn't
+    '''
+    bar_notes = len(bar)
+    count_rest = 0
+    for note in bar:
+        if note[0] == (-1,):
+            count_rest += 1
+    if count_rest == bar_notes:
+        return False
+    else:
+        return True
+      
+def round_to_sixteenth_note(x, base=0.25):
+        '''round value to closest multiplication by base
+        in default to 0.25 witch is sisteenth note accuracy 
+        '''
+        
+        return base * round(x/base)
+       
+def parse_pretty_midi_instrument(instrument, resolution, time_to_tick, key_offset):
+    ''' arguments: a prettyMidi instrument object
+        return: a custom SingleTrack object
+    '''
+       
+    first_tick = None
+    prev_tick = 0
+    prev_note_lenth = 0
+    max_rest_len = 4.0
+
+    notes = defaultdict(lambda:[set(), set()])
+    for note in instrument.notes:
+        if first_tick == None:
+            first_tick = 0
+            
+        tick = round_to_sixteenth_note(time_to_tick(note.start)/resolution)
+        if prev_tick != None:
+            act_tick = prev_tick + prev_note_lenth
+            if act_tick < tick:
+                rest_lenth = tick - act_tick
+                while rest_lenth > max_rest_len:
+                    notes[act_tick] = [{-1},{max_rest_len}]
+                    act_tick += max_rest_len
+                    rest_lenth -= max_rest_len
+                notes[act_tick] = [{-1},{rest_lenth}]
+
+        note_lenth = round_to_sixteenth_note(time_to_tick(note.end-note.start)/resolution)
+        
+        if -1 in notes[tick][0]:
+            notes[tick] = [set(), set()]
+        
+        if instrument.is_drum:
+            notes[tick][0].add(note.pitch)
+        else:
+            notes[tick][0].add(note.pitch+key_offset)
+            
+        notes[tick][1].add(note_lenth)
+
+        prev_tick = tick
+        prev_note_lenth = note_lenth
+    
+    notes = [(tuple(e[0]), max(e[1])) for e in notes.values()]
+
+    name = 'Drums' if instrument.is_drum else pm.program_to_instrument_class(instrument.program)
+    return SingleTrack(name, instrument.program, instrument.is_drum, Stream(first_tick,notes) )
+  
+def remove_duplicated_sequences(xy_tuple):
+    ''' removes duplicated x,y sequences
+    parameters:
+    -----------
+        xy_tuple: tuple of lists
+            tuple of x,y lists that represens sequances in training set
+            
+    return:
+    ------
+        x_unique, y_unique: tuple
+            a tuple of cleaned x, y traing set
+    '''
+    x = xy_tuple[0]
+    y = xy_tuple[1]
+    x_freeze = [tuple(seq) for seq in x]
+    y_freeze = [tuple(seq) for seq in y]
+    unique_data = list(set(zip(x_freeze,y_freeze)))
+    x_unique = [seq[0] for seq in unique_data]
+    y_unique = [seq[1] for seq in unique_data]
+    return x_unique, y_unique
+        
+        
+def extract_data(midi_folder_path=None, how=None, instrument=None, bar_in_seq=4, remove_duplicates=True):
     '''extract musical data from midis in given folder, to x_train, y_train lists on sequences
         
     parameters:
@@ -628,7 +580,7 @@ def extract_data(midi_folder_path=None, how=None, instrument=None, remove_duplic
         
     notes:
     ------
-        extracted data is transposed to the key od C
+        extracted data is transposed to the key of C
         duplicated x,y pairs are removed
     '''
     if how not in {'melody','arrangment'}:
@@ -637,40 +589,65 @@ def extract_data(midi_folder_path=None, how=None, instrument=None, remove_duplic
     x_train = []
     y_train = []
     
+    programs_for_instrument = []
+    
+    from collections import Counter
+
     for directory, subdirectories, files in os.walk(midi_folder_path):
-        for midi_file in tqdm(files):
+        for midi_file in tqdm(files, desc='Exporting: {}'.format(instrument)):
             midi_file_path = os.path.join(directory, midi_file)
             try:
                 mt = MultiTrack(midi_file_path)
+                # get programs
+                mt.get_programs(instrument)
+                
                 if how=='melody':
-                    x ,y = mt.get_data_seq2seq_melody(instrument)
+                    x ,y = mt.get_data_seq2seq_melody(instrument, bar_in_seq)
+                    programs_for_instrument.extend(mt.get_programs(instrument))
                 if how=='arrangment':
-                    x ,y = mt.get_data_seq2seq_arrangment(instrument[0], instrument[1])
+                    x ,y = mt.get_data_seq2seq_arrangment(instrument[0], instrument[1], bar_in_seq)
+                    programs_for_instrument.extend(mt.get_programs(instrument[1]))
                 x_train.extend(x)
                 y_train.extend(y)
             except:
                 continue
                 
+    most_recent_program = most_recent(programs_for_instrument)
+    
     if remove_duplicates:   
         x_train, y_train = remove_duplicated_sequences((x_train, y_train))
         
-    return x_train , y_train
+    return x_train , y_train, most_recent_program
   
+def most_recent(list): 
+    occurence_count = Counter(list) 
+    return occurence_count.most_common(1)[0][0] 
   
-# In[109]:
+def analyze_data(midi_folder_path):
+    '''Show usage of instumets in midipack
     
-
+    parameters:
-def main():
+    -----------
-    '''extract data from midis
+        midi_folder_path : string 
+            a path to directory where midi files are stored
     '''
-    x_train, y_train = extract_data(midi_folder_path='WhiteStripes', how='arrangment', instrument=('Guitar','Bass'))
-    pickle.dump((x_train, y_train), open('Guitar_to_Bass_data.pkl','wb'))
-    return x_train, y_train
     
+    instrument_count = dict()
+    instrument_programs = dict()
     
-# In[107]:
+    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)
+            try:
+                mt = MultiTrack(midi_file_path)
+                for track in mt.tracks:
+                    try:
+                        instrument_count[track.name] += len(get_bars_with_content(track.stream_to_bars()))
+                    except KeyError:
+                        instrument_count[track.name] = 1
+            except Exception as e:
+                print(e)
     
-
+    for key, value in sorted(instrument_count.items(), key=lambda x: x[1], reverse=True):
-if __name__=='__main__':
+        print(value, 'of', key)
-    main()
         

project/model.py

@@ -0,0 +1,370 @@
+from __future__ import print_function
+from midi_processing import stream_to_bars
+from keras.models import Model, load_model
+from keras.layers import Input, LSTM, Dense, LSTM, LSTMCell, TimeDistributed
+import numpy as np
+
+class Seq2SeqTransformer():
+    ''' encoder/transforer 
+    params:
+    -------
+        x_train, y_train - list of sequences
+
+    methods:
+        fit
+        transform'''
+    
+    def __init__(self):
+        self.transform_dict = None
+        self.reverse_dict = None
+        self.vocab_x = None
+        self.vocab_y = None
+        
+        
+    def preprocess(self, x_train, y_train):
+        '''Converts training set do list and add special chars'''
+
+        _x_train = []
+        for i, seq in enumerate(x_train):
+            _x_train.append([])
+            for note in seq:
+                _x_train[i].append(note)
+
+        _y_train = []
+        for i, seq in enumerate(y_train):
+            _y_train.append([])
+            _y_train[i].append('<GO>')
+            for note in seq:
+                    _y_train[i].append(note)
+            _y_train[i].append('<EOS>')
+                    
+        return _x_train, _y_train
+
+    def transform(self, x_train, y_train):
+        
+        x_vocab = set([note for seq in x_train for note in seq])
+        y_vocab = set([note for seq in y_train for note in seq])
+        
+        self.x_vocab = sorted(list(x_vocab)) 
+        self.y_vocab = ['<GO>','<EOS>']
+        self.y_vocab.extend(sorted(list(y_vocab)))
+
+        self.x_vocab_size = len(self.x_vocab)
+        self.y_vocab_size = len(self.y_vocab)
+
+        self.x_transform_dict = dict(
+            [(char, i) for i, char in enumerate(self.x_vocab)])
+        self.y_transform_dict = dict(
+            [(char, i) for i, char in enumerate(self.y_vocab)])
+        self.x_reverse_dict = dict(
+            (i, char) for char, i in self.x_transform_dict.items())
+        self.y_reverse_dict = dict(
+            (i, char) for char, i in self.y_transform_dict.items())
+               
+        x_train, y_train = self.preprocess(x_train, y_train)
+        
+        self.x_max_seq_length = max([len(seq) for seq in x_train])
+        self.y_max_seq_length = max([len(seq) for seq in y_train])
+        
+        encoder_input_data = np.zeros(
+            (len(x_train), self.x_max_seq_length, self.x_vocab_size),
+            dtype='float32')
+        decoder_input_data = np.zeros(
+            (len(x_train), self.y_max_seq_length, self.y_vocab_size),
+            dtype='float32')
+        decoder_target_data = np.zeros(
+            (len(x_train), self.y_max_seq_length, self.y_vocab_size),
+            dtype='float32')
+
+        for i, (x_train, y_train) in enumerate(zip(x_train, y_train)):
+            for t, char in enumerate(x_train):
+                encoder_input_data[i, t,  self.x_transform_dict[char]] = 1.
+            for t, char in enumerate(y_train):
+                decoder_input_data[i, t,  self.y_transform_dict[char]] = 1.
+                if t > 0:
+                    decoder_target_data[i, t - 1,  self.y_transform_dict[char]] = 1.
+                    
+        return encoder_input_data, decoder_input_data, decoder_target_data
+      
+      
+class Seq2SeqModel():
+    '''NeuralNerwork Seq2Seq model.
+    The network is created based on training data
+    '''
+
+    def __init__(self, latent_dim, x_train, y_train):
+        self.has_predict_model = False
+        self.has_train_model = False
+        self.x_train = x_train
+        self.y_train = y_train
+        self.latent_dim = latent_dim
+        self.transformer = Seq2SeqTransformer()
+        self.encoder_input_data, self.decoder_input_data, self.decoder_target_data = self.transformer.transform(self.x_train, self.y_train)
+    
+        # ---------------
+        # SEQ 2 SEQ MODEL:
+        #     INPUT_1 : encoder_input_data
+        #     INPUT_2 : decodet_input_data
+        #     OUTPUT : decoder_target_data
+        # ---------------
+        
+        # ENCODER MODEL
+        #---------------
+        
+        # 1 layer - Input : encoder_input_data
+        self.encoder_inputs = Input(shape=(None, self.transformer.x_vocab_size ))
+        
+        # 2 layer - LSTM_1, LSTM
+        self.encoder = LSTM(latent_dim, return_state=True)
+        #self.encoder = LSTM(latent_dim, return_state=True)
+        
+        # 2 layer - LSTM_1 : outputs
+        self.encoder_outputs, self.state_h, self.state_c = self.encoder(self.encoder_inputs)
+        self.encoder_states = [self.state_h, self.state_c]  
+        
+       
+        # DECODER MODEL
+        #---------------
+        
+        # 1 layer - Input : decoder_input_data
+        self.decoder_inputs = Input(shape=(None, self.transformer.y_vocab_size)) 
+        
+        # 2 layer - LSTM_1, LSTM
+        self.decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True)
+        #self.decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True)
+        
+        # 2 layer - LSTM_2 : outputs, full sequance as lstm layer
+        self.decoder_outputs, _, _ = self.decoder_lstm(self.decoder_inputs,                     
+                                                  initial_state=self.encoder_states)
+        
+        # 3 layer - Dense
+        self.decoder_dense = Dense(self.transformer.y_vocab_size, activation='softmax')
+        
+        # 3 layer - Dense : outputs, full sequance as the array of one-hot-encoded elements 
+        self.decoder_outputs = self.decoder_dense(self.decoder_outputs)
+        
+    def init_train_model(self):
+        self.train_model = Model([self.encoder_inputs, self.decoder_inputs], self.decoder_outputs)
+        self.train_model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
+        
+    def fit(self, batch_size, epochs, callbacks):     
+        
+        if not self.has_train_model:
+            self.init_train_model()
+            self.has_train_model = True
+        
+        
+        history = self.train_model.fit([self.encoder_input_data, self.decoder_input_data], self.decoder_target_data,
+                              batch_size=batch_size,
+                              epochs=epochs,
+                              callbacks=callbacks,
+                              validation_split=0.2)
+        return history
+        
+    def save(self, path):
+        self.train_model.save(path)
+    
+    def load(self, path):
+        self.train_model = load_model(path)
+        self.has_train_model = True
+        
+        self.encoder_inputs = self.train_model.layers[0].input
+        self.encoder = self.train_model.layers[2]
+        self.encoder_outputs, self.state_h, self.state_c = self.train_model.layers[2].output
+        self.encoder_states = [self.state_h, self.state_c]  
+        self.decoder_inputs = self.train_model.layers[1].input
+        self.decoder_lstm = self.train_model.layers[3]
+        self.decoder_outputs, _, _ = self.train_model.layers[3].output
+        self.decoder_dense = self.train_model.layers[4]
+        self.decoder_outputs = self.train_model.layers[4].output
+            
+    def init_predict_model(self):
+        
+        # ENCODER MODEL <- note used in develop music process
+        #     from encoder_input to encoder_states
+        #     to give a context to decoder model
+        #---------------------------------
+        
+        self.encoder_model = Model(self.encoder_inputs, self.encoder_states)
+
+        
+        # DECODER MODEL
+        #     From states (context) to sequance by generating firts element from context vector
+        #     and starting element <GO>. Then adding predicted element as input to next cell, with
+        #     updated states (context) by prevously generated element.
+        #   
+        #     INPUT_1 : state_h
+        #     INPUT_2 : state_c
+        #     INPUT_3 : y_train sized layer, that will be recursivly generated starting from <GO> sign
+        #     
+        #     INPUT -> LSTM -> DENSE
+        #
+        #     OUTPUT : one-hot-encoded element of sequance
+        #     OUTPUT : state_h (updated)
+        #     OUTPUT : state_c (updated)
+        # -------------
+        
+        # 1 layer: TWO INPUTS: decoder_state_h, decoder_state_c
+        self.decoder_state_input_h = Input(shape=(self.latent_dim,))  
+        self.decoder_state_input_c = Input(shape=(self.latent_dim,))
+        self.decoder_states_inputs = [self.decoder_state_input_h, self.decoder_state_input_c]
+        
+
+        # 2 layer: LSTM_1 output: element of sequance, lstm cell states
+        self.decoder_outputs, self.state_h, self.state_c = self.decoder_lstm(
+            self.decoder_inputs,
+            initial_state = self.decoder_states_inputs
+            )
+        
+        self.decoder_states = [self.state_h, self.state_c]
+        
+        # 3 layer: Dense output: one-hot-encoded representation of element of sequance
+        self.decoder_outputs = self.decoder_dense(self.decoder_outputs)
+
+        
+        self.decoder_model = Model(                               
+            [self.decoder_inputs] + self.decoder_states_inputs,
+            [self.decoder_outputs] + self.decoder_states)
+        self.has_predict_model = True
+        
+    def predict(self, input_seq=None, mode=None):
+        
+        if not self.has_predict_model:
+            self.init_predict_model()
+            self.has_predict_model = True
+        
+        if mode == 'generate':
+            # create a random context as starting point
+            h = np.random.rand(1,self.latent_dim)*2 - 1
+            c = np.random.rand(1,self.latent_dim)*2 - 1
+            states_value = [h, c]
+        else:
+            # get context from input sequance
+            states_value = self.encoder_model.predict(input_seq)
+
+        # make the empty decoder_input_data
+        # and create the starting <GO> element of decoder_input_data
+        target_seq = np.zeros((1, 1, self.transformer.y_vocab_size))  
+        target_seq[0, 0, self.transformer.y_transform_dict['<GO>']] = 1.
+
+        # sequance generation loop of decoder model
+        stop_condition = False
+        decoded_sentence = []
+#         time = 0
+        while not stop_condition:            
+            
+            # INPUT_1 : target_seq : started from empty array with start <GO> char
+            # and recursivly updated by predicted elements
+            
+            # INPUT_2 : states_value :context from encoder model or randomly generated in develop mode
+            # this can give as a 2 * latent_dim parameters to play with in manual generation
+            
+            # OUTPUT_1 : output_tokens : one hot encoded predicted element of sequance
+            # OUTPUT_2,3 : h, c : context updated by predicted element
+            output_tokens, h, c = self.decoder_model.predict(
+                [target_seq] + states_value)
+
+            # get most likly element index
+            # translate from index to final (in normal form) preidcted element
+            # append it to output list
+            sampled_token_index = np.argmax(output_tokens[0, -1, :])
+            sampled_char = self.transformer.y_reverse_dict[sampled_token_index]
+            decoded_sentence.append(sampled_char)
+            
+#             time += sampled_char[1]
+            # or time>=16
+            if (sampled_char == '<EOS>' or len(decoded_sentence) > self.transformer.y_max_seq_length ):
+                stop_condition = True
+
+            target_seq = np.zeros((1, 1, self.transformer.y_vocab_size))
+            target_seq[0, 0, sampled_token_index] = 1.
+
+            states_value = [h, c]
+
+        return decoded_sentence
+    
+    def develop(self, mode='from_seq'):
+        
+        # music generation for seq2seq for melody
+        input_seq_start = random_seed_generator(16,
+                                                self.transformer.x_max_seq_length,
+                                                self.transformer.x_vocab_size,
+                                                self.transformer.x_transform_dict,
+                                                self.transformer.x_reverse_dict)
+        
+        input_data = seq_to_numpy(input_seq_start,
+                                  self.transformer.x_max_seq_length,
+                                  self.transformer.x_vocab_size,
+                                  self.transformer.x_transform_dict)
+
+        # generate sequnce iterativly for melody
+        input_seq = input_seq_start.copy()
+        melody = []
+        for i in range(4):
+            if mode == 'from_seq':
+                decoded_sentence = self.predict(input_data)[:-1]
+            elif mode == 'from_state':
+                decoded_sentence = self.predict(mode='generate')[:-1]
+            else:
+                raise ValueError('mode must be in {from_seq, from_state}')
+            melody.append(decoded_sentence)
+            input_seq.extend(decoded_sentence)
+            input_bars = stream_to_bars(input_seq, 4)
+            input_bars = input_bars[1:5]
+            input_seq = [note for bar in input_bars for note in bar]
+            input_data = seq_to_numpy(input_seq,
+                                      self.transformer.x_max_seq_length,
+                                      self.transformer.x_vocab_size,
+                                      self.transformer.x_transform_dict)
+
+        melody = [note for bar in melody for note in bar]
+        return melody
+      
+def random_seed_generator(time_of_seq, max_encoder_seq_length, num_encoder_tokens, input_token_index, reverse_input_char_index):
+    time = 0
+    random_seq = []
+    items = 0
+    stop_sign = False
+    while (time < time_of_seq):
+        seed = np.random.randint(0,num_encoder_tokens-1)
+        note = reverse_input_char_index[seed]
+        time += note[1]
+        if time > time_of_seq:
+            note_time = note[1] - (time-time_of_seq)
+            trimmed_note = (note[0],note_time)
+            try:
+                seed = input_token_index[trimmed_note]
+                random_seq.append(trimmed_note)
+                items += 1
+            except KeyError:
+                time -= note[1]
+                continue
+        else:
+            random_seq.append(note)
+            items += 1
+        
+        if items > max_encoder_seq_length:
+            time = 0
+            random_seq = []
+            items = 0
+            stop_sign = False
+            
+        
+    return random_seq  
+  
+# seq to numpy array:
+def seq_to_numpy(seq, max_encoder_seq_length, num_encoder_tokens, input_token_index):
+    input_data = np.zeros(
+        (1, max_encoder_seq_length, num_encoder_tokens),
+        dtype='float32')
+
+    for t, char in enumerate(seq):
+        try:
+            input_data[0, t, input_token_index[char]] = 1.
+        except KeyError:
+            char_time = char[1]
+            _char = ((-1,), char_time)
+        except IndexError:
+            break
+
+    return input_data

project/readme.md

@@ -1,26 +1,12 @@
 ## MUSIC GENERATION USING DEEP LEARNING
 ## AUTHOR: CEZARY PUKOWNIK
 
-### Files:
- - midi.py - code for data extraction, and midi convertion
- - train.py - code for model definition, and training session
- - generate.py - code for model loading, predicting ang saving to midi_dir
- - settings.py - file where deafult settings are stored
- - readme.md - this file
-
-### Directories:
- - data/midi - directory where input midi are stored
- - data/models - directory where trained models are stored
- - data/output - directory where generated music is stored
- - data/samples - directory where extracted data from midi is stored
- - data/samples.npz - deprecated
-
 ## How to use:
-1. Use midi.py to export data from midi files
+1. In folder ./midi_packs make folder with midi files you want train on
-  > ./midi.py [midi_folder_path] [output_path]
+2. Use extract.py to export data from midis 
+  > ./extract.py [str: midi_pack_name] [str: name_of_session] --b [int: seq_len] -a [analize data first]
+3. Use train.py to train model
+  > ./train.py [str: name_of_session] --b [int: batch_size] --l [int: latent_space] --e [int: epochs] --i [str: instrument] -r [reset]
+4. Use generate.py to generate music from models
+  > ./generate.py [str: name_of_session] --n [number of generations] --m [mode {'from_seq','from_state'}]
 
-2. Use train.py to train a model (this can take a while)
-  > ./train.py [input_training_data] [model_save_path] [epochs]
-
-3. Use generate.py to generate music from trained models
-  > ./generate.py [trained_model_path] [output_path] [treshold]

project/settings.py

@@ -1,313 +0,0 @@
-# paths
-midi_dir = 'data/midi'
-samples_dir = 'data/samples'
-samples_path = 'data/samples.npz'
-sample_preview_path = 'data/samples_preview.png'
-model_path = 'data/autoencoder_model.h5'
-generated_sample_path = 'data/output/generated_bar.npz'
-generated_midi_path = 'data/output/generated_midi.mid'
-generated_pianoroll_path = 'data/output/pianoroll.png'
-
-# export_settings
-midi_resolution = 96
-beat_resolution = 24
-beats_per_sample = 1
-ignore_note_lenght = False
-
-#train_settings
-epochs = 1
-
-#extras
-midi_program = {
-# Piano
-    1 : 'Acoustic Grand Piano',
-    2 : 'Bright Acoustic Piano',
-    3 : 'Electric Grand Piano',
-    4 : 'Honky-tonk Piano',
-    5 : 'Electric Piano 1',
-    6 : 'Electric Piano 2',
-    7 : 'Harpsichord',
-    8 : 'Clavi',
-# Chromatic Percussion
-    9 : 'Celesta',
-    10 : 'Glockenspiel',
-    11 : 'Music Box',
-    12 : 'Vibraphone',
-    13 : 'Marimba',
-    14 : 'Xylophone',
-    15 : 'Tubular Bells',
-    16 : 'Dulcimer',
-# Organ
-    17 : 'Drawbar Organ',
-    18 : 'Percussive Organ',
-    19 : 'Rock Organ',
-    20 : 'Church Organ',
-    21 : 'Reed Organ',
-    22 : 'Accordion',
-    23 : 'Harmonica',
-    24 : 'Tango Accordion',
-# Guitar
-    25 : 'Acoustic Guitar (nylon)',
-    26 : 'Acoustic Guitar (steel)',
-    27 : 'Electric Guitar (jazz)',
-    28 : 'Electric Guitar (clean)',
-    29 : 'Electric Guitar (muted)',
-    30 : 'Overdriven Guitar',
-    31 : 'Distortion Guitar',
-    32 : 'Guitar harmonics',
-# Bass
-    33 : 'Acoustic Bass',
-    34 : 'Electric Bass (finger)',
-    35 : 'Electric Bass (pick)',
-    36 : 'Fretless Bass',
-    37 : 'Slap Bass 1',
-    38 : 'Slap Bass 2',
-    39 : 'Synth Bass 1',
-    40 : 'Synth Bass 2',
-# Strings
-    41 : 'Violin',
-    42 : 'Viola',
-    43 : 'Cello',
-    44 : 'Contrabass',
-    45 : 'Tremolo Strings',
-    46 : 'Pizzicato Strings',
-    47 : 'Orchestral Harp',
-    48 : 'Timpani',
-# Ensemble
-    49 : 'String Ensemble 1',
-    50 : 'String Ensemble 2',
-    51 : 'SynthStrings 1',
-    52 : 'SynthStrings 2',
-    53 : 'Choir Aahs',
-    54 : 'Voice Oohs',
-    55 : 'Synth Voice',
-    56 : 'Orchestra Hit',
-# Brass
-    57 : 'Trumpet',
-    58 : 'Trombone',
-    59 : 'Tuba',
-    60 : 'Muted Trumpet',
-    61 : 'French Horn',
-    62 : 'Brass Section',
-    63 : 'SynthBrass 1',
-    64 : 'SynthBrass 2',
-# Reed
-    65 : 'Soprano Sax',
-    66 : 'Alto Sax',
-    67 : 'Tenor Sax',
-    68 : 'Baritone Sax',
-    69 : 'Oboe',
-    70 : 'English Horn',
-    71 : 'Bassoon',
-    72 : 'Clarinet',
-# Pipe
-    73 : 'Piccolo',
-    74 : 'Flute',
-    75 : 'Recorder',
-    76 : 'Pan Flute',
-    77 : 'Blown Bottle',
-    78 : 'Shakuhachi',
-    79 : 'Whistle',
-    80 : 'Ocarina',
-# Synth Lead
-    81 : 'Lead 1 (square)',
-    82 : 'Lead 2 (sawtooth)',
-    83 : 'Lead 3 (calliope)',
-    84 : 'Lead 4 (chiff)',
-    85 : 'Lead 5 (charang)',
-    86 : 'Lead 6 (voice)',
-    87 : 'Lead 7 (fifths)',
-    88 : 'Lead 8 (bass + lead)',
-# Synth Pad
-    89 : 'Pad 1 (new age)',
-    90 : 'Pad 2 (warm)',
-    91 : 'Pad 3 (polysynth)',
-    92 : 'Pad 4 (choir)',
-    93 : 'Pad 5 (bowed)',
-    94 : 'Pad 6 (metallic)',
-    95 : 'Pad 7 (halo)',
-    96 : 'Pad 8 (sweep)',
-# Synth Effects
-    97 : 'FX 1 (rain)',
-    98 : 'FX 2 (soundtrack)',
-    99 : 'FX 3 (crystal)',
-    100 : 'FX 4 (atmosphere)',
-    101 : 'FX 5 (brightness)',
-    102 : 'FX 6 (goblins)',
-    103 : 'FX 7 (echoes)',
-    104 : 'FX 8 (sci-fi)',
-# Ethnic
-    105 : 'Sitar',
-    106 : 'Banjo',
-    107 : 'Shamisen',
-    108 : 'Koto',
-    109 : 'Kalimba',
-    110 : 'Bag pipe',
-    111 : 'Fiddle',
-    112 : 'Shanai',
-# Percussive
-    113 : 'Tinkle Bell',
-    114 : 'Agogo',
-    115 : 'Steel Drums',
-    116 : 'Woodblock',
-    117 : 'Taiko Drum',
-    118 : 'Melodic Tom',
-    119 : 'Synth Drum',
-    120 : 'Reverse Cymbal',
-# Sound Effects
-    121 : 'Guitar Fret Noise',
-    122 : 'Breath Noise',
-    123 : 'Seashore',
-    124 : 'Bird Tweet',
-    125 : 'Telephone Ring',
-    126 : 'Helicopter',
-    127 : 'Applause',
-    128 : 'Gunshot'
-}
-
-midi_group = {
-# Piano
-    1 : 'Piano',
-    2 : 'Piano',
-    3 : 'Piano',
-    4 : 'Piano',
-    5 : 'Piano',
-    6 : 'Piano',
-    7 : 'Piano',
-    8 : 'Piano',
-# Chromatic Percussion
-    9 : 'Chromatic_Percussion',
-    10 : 'Chromatic_Percussion',
-    11 : 'Chromatic_Percussion',
-    12 : 'Chromatic_Percussion',
-    13 : 'Chromatic_Percussion',
-    14 : 'Chromatic_Percussion',
-    15 : 'Chromatic_Percussion',
-    16 : 'Chromatic_Percussion',
-# Organ
-    17 : 'Organ',
-    18 : 'Organ',
-    19 : 'Organ',
-    20 : 'Organ',
-    21 : 'Organ',
-    22 : 'Organ',
-    23 : 'Organ',
-    24 : 'Organ',
-# Guitar
-    25 : 'Guitar',
-    26 : 'Guitar',
-    27 : 'Guitar',
-    28 : 'Guitar',
-    29 : 'Guitar',
-    30 : 'Guitar',
-    31 : 'Guitar',
-    32 : 'Guitar',
-# Bass
-    33 : 'Bass',
-    34 : 'Bass',
-    35 : 'Bass',
-    36 : 'Bass',
-    37 : 'Bass',
-    38 : 'Bass',
-    39 : 'Bass',
-    40 : 'Bass',
-# Strings
-    41 : 'Strings',
-    42 : 'Strings',
-    43 : 'Strings',
-    44 : 'Strings',
-    45 : 'Strings',
-    46 : 'Strings',
-    47 : 'Strings',
-    48 : 'Strings',
-# Ensemble
-    49 : 'Ensemble',
-    50 : 'Ensemble',
-    51 : 'Ensemble',
-    52 : 'Ensemble',
-    53 : 'Ensemble',
-    54 : 'Ensemble',
-    55 : 'Ensemblee',
-    56 : 'Ensemble',
-# Brass
-    57 : 'Brass',
-    58 : 'Brass',
-    59 : 'Brass',
-    60 : 'Brass',
-    61 : 'Brass',
-    62 : 'Brass',
-    63 : 'Brass',
-    64 : 'Brass',
-# Reed
-    65 : 'Reed',
-    66 : 'Reed',
-    67 : 'Reed',
-    68 : 'Reed',
-    69 : 'Reed',
-    70 : 'Reed',
-    71 : 'Reed',
-    72 : 'Reed',
-# Pipe
-    73 : 'Pipe',
-    74 : 'Pipe',
-    75 : 'Pipe',
-    76 : 'Pipe',
-    77 : 'Pipe',
-    78 : 'Pipe',
-    79 : 'Pipe',
-    80 : 'Pipe',
-# Synth Lead
-    81 : 'Synth_Lead',
-    82 : 'Synth_Lead',
-    83 : 'Synth_Lead',
-    84 : 'Synth_Lead',
-    85 : 'Synth_Lead',
-    86 : 'Synth_Lead',
-    87 : 'Synth_Lead',
-    88 : 'Synth_Lead',
-# Synth Pad
-    89 : 'Synth_Pad',
-    90 : 'Synth_Pad',
-    91 : 'Synth_Pad',
-    92 : 'Synth_Pad',
-    93 : 'Synth_Pad',
-    94 : 'Synth_Pad',
-    95 : 'Synth_Pad',
-    96 : 'Synth_Pad',
-# Synth Effects
-    97 : 'Synth_Effects',
-    98 : 'Synth_Effects',
-    99 : 'Synth_Effects',
-    100 : 'Synth_Effects',
-    101 : 'Synth_Effects',
-    102 : 'Synth_Effects',
-    103 : 'Synth_Effects',
-    104 : 'Synth_Effects',
-# Ethnic
-    105 : 'Ethnic',
-    106 : 'Ethnic',
-    107 : 'Ethnic',
-    108 : 'Ethnic',
-    109 : 'Ethnic',
-    110 : 'Ethnic',
-    111 : 'Ethnic',
-    112 : 'Ethnic',
-# Percussive
-    113 : 'Percussive',
-    114 : 'Percussive',
-    115 : 'Percussive',
-    116 : 'Percussive',
-    117 : 'Percussive',
-    118 : 'Percussive',
-    119 : 'Percussive',
-    120 : 'Percussive',
-# Sound Effects
-    121 : 'Sound_Effects',
-    122 : 'Sound_Effects',
-    123 : 'Sound_Effects',
-    124 : 'Sound_Effects',
-    125 : 'Sound_Effects',
-    126 : 'Sound_Effects',
-    127 : 'Sound_Effects',
-    128 : 'Sound_Effects'
-}

project/train.py

@@ -1,68 +1,79 @@
-#!python3
+import os
-#!/usr/bin/env python3
 import sys
 import pickle
-import settings
+import keras
+import argparse
+import warnings
+from model import Seq2SeqModel
+from extract import make_folder_if_not_exist
 
-import numpy as np
+# TODO: 
-from keras.layers import Input, Dense, Conv2D, Flatten, LSTM, Dropout, TimeDistributed, RepeatVector, Activation, Bidirectional, Reshape
+# FIXME:
-from keras.models import Model, Sequential
-from keras.utils.np_utils import to_categorical
 
+def parse_argv():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('n', help='name for experiment', type=str)
+    parser.add_argument('--b', help='batch_size', type=int)
+    parser.add_argument('--l', help='latent_dim', type=int)
+    parser.add_argument('--e', help='epochs', type=int)
+    parser.add_argument('--i', help='refrance to instrument to train, if you want to train only one instument')
+    parser.add_argument('-r', help='reset, use when you want to reset waights and train from scratch', action='store_true')
+    args = parser.parse_args()
+    return args
 
-def load_data(samples_path):
+def load_workflow():
-    print('Loading... {}'.format(train_data_path))
+    workflow_path = os.path.join('training_sets', EXPERIMENT_NAME, 'workflow.pkl')
-    train_X = np.load(train_data_path, allow_pickle=True)['arr_0']
+    if os.path.isfile(workflow_path):
-    train_y = np.load(train_data_path, allow_pickle=True)['arr_1']
+        model_workflow = pickle.load(open(workflow_path,'rb'))
-    return train_X, train_y
+    else:
+        raise FileNotFoundError(f'There is no workflow.pkl file in trainig_sets/{EXPERIMENT_NAME}/ folder')
+    return model_workflow
 
-# TODO: make transformer class with fit, transform and reverse definitions
+def train_models(model_workflow):
-def preprocess_samples(train_X, train_y):
-    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))
-    _train_X = []
-    _train_y = []
-    for sample in train_X:
-        # TODO: add normalizasion
-        _train_X.append([note_to_int[note] for note in sample])
     
-    train_X = np.array(_train_X).reshape(train_X.shape[0], train_X.shape[1], 1)
+    instruments = [instrument if how == 'melody' else instrument[1] for key, (instrument, how) in model_workflow.items()]
-    train_y = np.array([note_to_int[note] for note in train_y]).reshape(-1,1)
+    # make_folder_if_not_exist(os.mkdir(os.path.join('models',EXPERIMENT_NAME)))
-    train_y = to_categorical(train_y)
     
-    return train_X, train_y, n_vocab, int_to_note
+    found = False
+    for instrument in instruments:
 
-train_data_path = sys.argv[1]
+        if INSTRUMENT == None or INSTRUMENT == instrument:
+            data_path = os.path.join('training_sets', EXPERIMENT_NAME, instrument.lower() + '_data.pkl')
+            model_path = os.path.join('models', EXPERIMENT_NAME, f'{instrument.lower()}_model.h5')
 
-train_X, train_y = load_data(train_data_path)
+            x_train, y_train, _ = pickle.load(open(data_path,'rb'))
-train_X, train_y, n_vocab, int_to_note = preprocess_samples(train_X, train_y)
+            model = Seq2SeqModel(LATENT_DIM, x_train, y_train)
+            if os.path.isfile(model_path) and not RESET:
+                model.load(model_path)
 
-save_model_path = sys.argv[2]
+            print(f'Training: {instrument}')
-epochs = int(sys.argv[3])
+            model.fit(BATCH_SIZE, EPOCHS, callbacks=[])
+            model.save(model_path)
+            found = True
 
-model = Sequential()
+    if not found:
-model.add(LSTM(512, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True))
+        raise ValueError(f'Instrument not found. Use one of the {instruments}')
-model.add(Dropout(0.3))
-model.add(LSTM(512, return_sequences=True))
-model.add(Dropout(0.3))
-model.add(LSTM(512))
-model.add(Dense(256))
-model.add(Dropout(0.3))
-model.add(Dense(n_vocab))
-model.add(Activation('softmax'))
-model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
     
-# This code will train our model, with given by parameter number of epochs
+if __name__ == '__main__':
-print('Training...')
-model.fit(train_X, train_y, epochs=epochs, batch_size=64)
 
-# it saves model, and additional informations of model 
+    warnings.filterwarnings("ignore")
-# that is needed to generate music from it 
+    args = parse_argv()
-pickle.dump(model, open(save_model_path,'wb'))
+    
-pickle.dump((int_to_note, n_vocab, train_X.shape[1]), open('{}_dict'.format(save_model_path),'wb'))
+    EXPERIMENT_NAME = args.n
-print('Done!')
+    BATCH_SIZE = args.b
-print("Model saved to: {}".format(save_model_path))
+    LATENT_DIM = args.l
+    EPOCHS = args.e
+    RESET = args.r
+    INSTRUMENT = args.i
+
+    # default settings if not args passed
+    if not BATCH_SIZE:
+        BATCH_SIZE = 32
+    if not LATENT_DIM:
+        LATENT_DIM = 256
+    if not EPOCHS:
+        EPOCHS = 1
+    if not RESET:
+        RESET = False
+    
+    train_models(load_workflow())