97 lines
3.3 KiB
Python
97 lines
3.3 KiB
Python
from midi_processing import MultiTrack, SingleTrack, Stream
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from model import Seq2SeqModel, seq_to_numpy
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from tqdm import tqdm
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import argparse
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import os
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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 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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for key, value in model_workflow.items():
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if isinstance(value[0], str):
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instrument = value[0]
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generator = None
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else:
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instrument = value[0][1]
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generator = value[0][0]
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band[instrument] = [None, None, generator]
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'''LOAD MODELS'''
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print('Loading models...')
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for instrument in tqdm(band):
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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, instrument.lower() + '_model.h5')
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x_train, y_train, program, bars_in_seq = pickle.load(open(data_path,'rb'))
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model = Seq2SeqModel(x_train, y_train, bars_in_seq=bars_in_seq)
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model.load(model_path)
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band[instrument][0] = model
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band[instrument][1] = program
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print('Generating music...')
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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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for instrument, (model, program, generator) in band.items():
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if generator == None:
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notes[instrument] = model.develop(mode=MODE)
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else:
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input_data = seq_to_numpy(notes[generator],
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model.transformer.x_max_seq_length,
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model.transformer.x_vocab_size,
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model.transformer.x_transform_dict)
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notes[instrument] = model.predict(input_data)[:-1]
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'''COMPILE TO MIDI'''
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generated_midi = MultiTrack()
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for instrument, (model, program, generator) in band.items():
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if instrument == 'Drums':
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is_drums = True
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else:
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is_drums = False
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stream = Stream(first_tick=0, notes=notes[instrument])
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track = SingleTrack(name=instrument ,program=program, is_drum=is_drums, stream=stream)
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generated_midi.tracks.append(track)
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# make folder for new experiment
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try:
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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, SESSION_NAME, f'{EXPERIMENT_NAME}_{midi_counter}_{MODE}.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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