code clarity
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@ -5,54 +5,70 @@ import pickle
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from midi_processing import extract_data, analyze_data
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parser = argparse.ArgumentParser()
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parser.add_argument('midi_pack', help='folder name for midi pack in midi_packs folder', type=str)
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parser.add_argument('name', help='name for experiment', type=str)
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parser.add_argument('--b', help='lengh of sequence in bars', type=int)
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parser.add_argument('-a', help='analize data', action='store_true')
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args = parser.parse_args()
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def make_folder_if_not_exist(path):
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try:
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os.mkdir(path)
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except:
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pass
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'''SETTINGS'''
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MIDI_PACK_NAME = args.midi_pack
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EXPERIMENT_NAME = args.name
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BARS_IN_SEQ = args.b
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def parse_argv():
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parser = argparse.ArgumentParser()
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parser.add_argument('midi_pack', help='folder name for midi pack in midi_packs folder', type=str)
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parser.add_argument('--n', help='name for experiment', type=str)
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parser.add_argument('--b', help='lengh of sequence in bars', type=int)
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parser.add_argument('-a', help='analize data', action='store_true')
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args = parser.parse_args()
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return args
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midi_folder_path = os.path.join('midi_packs', MIDI_PACK_NAME)
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def ask_for_workflow():
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'''MODEL WORKFLOW DIALOG'''
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number_of_instruments = int(input('Please specify number of instruments\n'))
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model_workflow = dict()
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for i in range(number_of_instruments):
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input_string = input('Please specify a workflow step <Instrument> [<Second Instrument>] <mode> {m : melody, a : arrangment}\n')
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tokens = input_string.split()
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if tokens[-1] == 'm':
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model_workflow[i] = (tokens[0], 'melody')
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elif tokens[-1] == 'a':
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model_workflow[i] = ((tokens[1], tokens[0]), 'arrangment')
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else:
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raise ValueError("The step definitiom must end with {'m', 'a'}");
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make_folder_if_not_exist(os.path.join('training_sets', EXPERIMENT_NAME))
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pickle.dump(model_workflow, open(os.path.join('training_sets', EXPERIMENT_NAME, 'workflow.pkl'),'wb'))
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return model_workflow
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# analyze data set for intresting intruments
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if args.a:
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analyze_data(midi_folder_path)
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sys.exit()
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def extract_from_folder(model_workflow):
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for key, (instrument, how) in model_workflow.items():
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if how == 'melody':
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instrument_name = instrument
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else:
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instrument_name = instrument[1]
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make_folder_if_not_exist(os.path.join('training_sets', EXPERIMENT_NAME))
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save_path = os.path.join('training_sets', EXPERIMENT_NAME, instrument_name.lower() + '_data.pkl')
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x_train, y_train, program = extract_data(midi_folder_path=os.path.join('midi_packs', MIDI_PACK_NAME),
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how=how,
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instrument=instrument,
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bar_in_seq=BARS_IN_SEQ)
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pickle.dump((x_train, y_train, program), open(save_path,'wb'))
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if __name__ == '__main__':
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args = parse_argv()
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'''MODEL WORKFLOW DIALOG'''
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number_of_instruments = int(input('Please specify number of instruments\n'))
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model_workflow = dict()
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input_list = []
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for i in range(number_of_instruments):
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input_string = input('Please specify a workflow step\n')
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tokens = input_string.split()
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if tokens[-1] == 'melody':
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model_workflow[i] = (tokens[0], tokens[1])
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MIDI_PACK_NAME = args.midi_pack
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EXPERIMENT_NAME = args.n
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BARS_IN_SEQ = args.b
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if not EXPERIMENT_NAME:
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EXPERIMENT_NAME = MIDI_PACK_NAME
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if not BARS_IN_SEQ:
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BARS_IN_SEQ = 4
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ANALIZE = args.a
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if ANALIZE:
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analyze_data(os.path.join('midi_packs', MIDI_PACK_NAME))
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else:
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model_workflow[i] = ((tokens[1], tokens[0]), tokens[2])
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# make folder for new experiment if no exist
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try:
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os.mkdir(os.path.join('training_sets', EXPERIMENT_NAME))
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except:
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pass
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# extract process
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for key, (instrument, how) in model_workflow.items():
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if how == 'melody':
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instrument_name = instrument
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else:
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instrument_name = instrument[1]
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save_path = os.path.join('training_sets', EXPERIMENT_NAME, instrument_name.lower() + '_data.pkl')
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x_train, y_train, program = extract_data(midi_folder_path=midi_folder_path, how=how,
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instrument=instrument, bar_in_seq=BARS_IN_SEQ)
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pickle.dump((x_train, y_train, program), open(save_path,'wb'))
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pickle.dump(model_workflow, open(os.path.join('training_sets', EXPERIMENT_NAME, 'workflow.pkl'),'wb'))
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extract_from_folder(ask_for_workflow())
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@ -7,19 +7,27 @@ import pickle
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parser = argparse.ArgumentParser()
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parser.add_argument('n', help='name for experiment', type=str)
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parser.add_argument('s', help='session name', type=str)
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parser.add_argument('--i', help='number of midis to generate', type=int)
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parser.add_argument('--l', help='latent_dim_of_model', type=int)
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parser.add_argument('--m', help="mode {'from_seq', 'from_state}'", type=str)
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args = parser.parse_args()
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EXPERIMENT_NAME = args.n
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SESSION_NAME = args.s
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GENERETIONS_COUNT = args.i
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LATENT_DIM = args.l
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MODE = args.m
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if GENERETIONS_COUNT == None:
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if not GENERETIONS_COUNT:
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GENERETIONS_COUNT = 1
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if not LATENT_DIM:
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LATENT_DIM = 256
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if not MODE:
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MODE = 'from_seq'
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model_workflow = pickle.load(open(os.path.join('training_sets', EXPERIMENT_NAME, 'workflow.pkl'),'rb'))
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band = dict()
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@ -45,7 +53,7 @@ for instrument in tqdm(band):
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band[instrument][0] = model
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band[instrument][1] = program
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for midi_counter in range(GENERETIONS_COUNT):
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for midi_counter in tqdm(range(GENERETIONS_COUNT)):
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''' MAKE MULTIINSTRUMENTAL MUSIC !!!'''
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notes = dict()
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@ -76,7 +84,11 @@ for midi_counter in range(GENERETIONS_COUNT):
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os.mkdir(os.path.join('generated_music', EXPERIMENT_NAME))
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except:
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pass
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try:
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os.mkdir(os.path.join('generated_music', EXPERIMENT_NAME, SESSION_NAME))
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except:
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pass
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save_path = os.path.join('generated_music', EXPERIMENT_NAME, f'{EXPERIMENT_NAME}_{midi_counter}_{MODE}_{LATENT_DIM}.mid')
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save_path = os.path.join('generated_music', EXPERIMENT_NAME, SESSION_NAME, f'{EXPERIMENT_NAME}_{midi_counter}_{MODE}_{LATENT_DIM}.mid')
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generated_midi.save(save_path)
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print(f'Generated succefuly to {save_path}')
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# print(f'Generated succefuly to {save_path}')
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@ -10,8 +10,6 @@ from random import randint
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import pretty_midi as pm
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from tqdm import tqdm
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# TODO: Stream class is no logner needed <- remore from code and make just SingleTrack.notes instead on SingleTrack.stream.notes
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class Stream():
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@ -486,7 +484,7 @@ def round_to_sixteenth_note(x, base=0.25):
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'''
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return base * round(x/base)
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def parse_pretty_midi_instrument(instrument, resolution, time_to_tick, key_offset):
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''' arguments: a prettyMidi instrument object
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return: a custom SingleTrack object
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@ -52,7 +52,6 @@ class Seq2SeqTransformer():
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self.x_vocab_size = len(self.x_vocab)
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self.y_vocab_size = len(self.y_vocab)
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self.x_transform_dict = dict(
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[(char, i) for i, char in enumerate(self.x_vocab)])
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self.y_transform_dict = dict(
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113
project/train.py
113
project/train.py
@ -3,64 +3,77 @@ import sys
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import pickle
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import keras
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import argparse
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import warnings
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from model import Seq2SeqModel
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from extract import make_folder_if_not_exist
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parser = argparse.ArgumentParser()
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parser.add_argument('n', help='name for experiment', type=str)
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parser.add_argument('--b', help='batch_size', type=int)
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parser.add_argument('--l', help='latent_dim', type=int)
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parser.add_argument('--e', help='epochs', type=int)
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parser.add_argument('--r', help='reset, use when you want to reset waights and train from scratch', action='store_true')
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parser.add_argument('--i', help='refrance to instrument to train, if you want to train only one instument')
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args = parser.parse_args()
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# TODO:
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# FIXME:
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def parse_argv():
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parser = argparse.ArgumentParser()
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parser.add_argument('n', help='name for experiment', type=str)
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parser.add_argument('--b', help='batch_size', type=int)
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parser.add_argument('--l', help='latent_dim', type=int)
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parser.add_argument('--e', help='epochs', type=int)
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parser.add_argument('--i', help='refrance to instrument to train, if you want to train only one instument')
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parser.add_argument('-r', help='reset, use when you want to reset waights and train from scratch', action='store_true')
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args = parser.parse_args()
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return args
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'''HYPER PARAMETERS'''
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EXPERIMENT_NAME = args.n
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BATCH_SIZE = args.b
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LATENT_DIM = args.l
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EPOCHS = args.e
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RESET = args.r
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INSTRUMENT = args.i
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def load_workflow():
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workflow_path = os.path.join('training_sets', EXPERIMENT_NAME, 'workflow.pkl')
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if os.path.isfile(workflow_path):
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model_workflow = pickle.load(open(workflow_path,'rb'))
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else:
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raise FileNotFoundError(f'There is no workflow.pkl file in trainig_sets/{EXPERIMENT_NAME}/ folder')
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return model_workflow
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if BATCH_SIZE == None:
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BATCH_SIZE = 32
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if LATENT_DIM == None:
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LATENT_DIM = 256
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if EPOCHS == None:
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EPOCHS = 1
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if RESET == None:
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RESET = False
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def train_models(model_workflow):
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instruments = [instrument if how == 'melody' else instrument[1] for key, (instrument, how) in model_workflow.items()]
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# make_folder_if_not_exist(os.mkdir(os.path.join('models',EXPERIMENT_NAME)))
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found = False
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for instrument in instruments:
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## TODO: raise error if file not found
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model_workflow = pickle.load(open(os.path.join('training_sets', EXPERIMENT_NAME, 'workflow.pkl'),'rb'))
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tbCallBack = keras.callbacks.TensorBoard(log_dir='./Graph', histogram_freq=0, write_graph=True, write_images=True)
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if INSTRUMENT == None or INSTRUMENT == instrument:
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data_path = os.path.join('training_sets', EXPERIMENT_NAME, instrument.lower() + '_data.pkl')
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model_path = os.path.join('models', EXPERIMENT_NAME, f'{instrument.lower()}_model.h5')
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instruments = [instrument if how == 'melody' else instrument[1] for key, (instrument, how) in model_workflow.items()]
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x_train, y_train, _ = pickle.load(open(data_path,'rb'))
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model = Seq2SeqModel(LATENT_DIM, x_train, y_train)
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if os.path.isfile(model_path) and not RESET:
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model.load(model_path)
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# make folder for new experiment
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try:
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os.mkdir(os.path.join('models',EXPERIMENT_NAME))
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except:
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pass
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print(f'Training: {instrument}')
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model.fit(BATCH_SIZE, EPOCHS, callbacks=[])
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model.save(model_path)
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found = True
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# init models
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found = False
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for instrument in instruments:
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if not found:
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raise ValueError(f'Instrument not found. Use one of the {instruments}')
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if __name__ == '__main__':
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if INSTRUMENT == None or INSTRUMENT == instrument:
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data_path = os.path.join('training_sets', EXPERIMENT_NAME, instrument.lower() + '_data.pkl')
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model_path = os.path.join('models', EXPERIMENT_NAME, f'{instrument.lower()}_model.h5')
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warnings.filterwarnings("ignore")
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args = parse_argv()
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EXPERIMENT_NAME = args.n
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BATCH_SIZE = args.b
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LATENT_DIM = args.l
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EPOCHS = args.e
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RESET = args.r
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INSTRUMENT = args.i
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x_train, y_train, _ = pickle.load(open(data_path,'rb'))
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model = Seq2SeqModel(LATENT_DIM, x_train, y_train)
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if os.path.isfile(model_path) and not RESET:
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model.load(model_path)
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print(f'Training: {instrument}')
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train_history = model.fit(BATCH_SIZE, EPOCHS, callbacks=[tbCallBack])
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model.save(model_path)
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found = True
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if not found:
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raise ValueError(f'Instrument not found. Use one of the {instruments}')
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# default settings if not args passed
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if not BATCH_SIZE:
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BATCH_SIZE = 32
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if not LATENT_DIM:
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LATENT_DIM = 256
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if not EPOCHS:
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EPOCHS = 1
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if not RESET:
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RESET = False
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train_models(load_workflow())
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