File size: 5,792 Bytes
151b875 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 | import os, csv, time
from argparse import ArgumentParser
import numpy as np
from transformers import AutoModelForCausalLM
from anticipation import ops
from anticipation.visuals import visualize
from anticipation.sample import generate, generate_ar
from anticipation.tokenize import extract_instruments
from anticipation.convert import midi_to_events, events_to_midi
from anticipation.config import TIME_RESOLUTION
np.random.seed(0)
def main(args):
if args.anticipatory or args.baseline:
print(f'Accompaniment using model checkpoint: {args.model}')
t0 = time.time()
model = AutoModelForCausalLM.from_pretrained(args.model).cuda()
print(f'Loaded model ({time.time()-t0} seconds)')
if args.anticipatory:
print(f'Writing outputs to {args.dir}/anticipatory')
try:
os.makedirs(f'{args.dir}/anticipatory')
except FileExistsError:
pass
if args.baseline:
print(f'Writing outputs to {args.dir}/autoregressive')
try:
os.makedirs(f'{args.dir}/autoregressive')
except FileExistsError:
pass
if args.retrieve:
print(f'Writing outputs to {args.dir}/retrieved')
try:
os.makedirs(f'{args.dir}/retrieved')
except FileExistsError:
pass
print(f'Accompanying tracks in index : {args.dir}/index.csv')
with open(f'{args.dir}/index.csv', newline='') as f:
reader = csv.reader(f)
header = next(reader)
for row in reader:
original = os.path.join(args.midis, row[header.index('original')])
conditional_midi = row[header.index('conditional')]
melody = int(row[header.index('melody')])
idx = int(row[header.index('idx')])
events = midi_to_events(os.path.join(args.dir, conditional_midi))
events, controls = extract_instruments(events, [melody])
prompt = ops.clip(events, 0, args.prompt_length, clip_duration=False)
for j in range(args.multiplicity):
t0 = time.time()
if args.anticipatory:
generated_tokens = generate(model, args.prompt_length, args.clip_length, prompt, controls, top_p=0.95)
output = ops.clip(ops.combine(generated_tokens, controls), 0, args.clip_length)
mid = events_to_midi(output)
mid.save(f'{args.dir}/anticipatory/{idx}-clip-v{j}.mid')
if args.visualize:
visualize(output, f'{args.dir}/anticipatory/{idx}-clip-v{j}.png')
if args.baseline:
generated_tokens = generate_ar(model, args.prompt_length, args.clip_length, prompt, controls, top_p=0.95)
output = ops.clip(generated_tokens, 0, args.clip_length)
print(len(generated_tokens), len(output))
mid = events_to_midi(output)
mid.save(f'{args.dir}/autoregressive/{idx}-clip-v{j}.mid')
if args.visualize:
visualize(output, f'{args.dir}/autoregressive/{idx}-clip-v{j}.png')
if args.retrieve:
original_events = midi_to_events(original)
max_time = ops.max_time(original_events) - args.clip_length
start_time = max_time*np.random.rand(1)[0] # get a different random clip
retrieved = ops.clip(original_events, start_time, start_time+args.clip_length, clip_duration=True)
retrieved = ops.translate(retrieved, -int(TIME_RESOLUTION*start_time))
events, _ = extract_instruments(retrieved, [melody])
generated = prompt + ops.clip(events, args.prompt_length, args.clip_length)
output = ops.combine(generated, controls)
mid = events_to_midi(output)
mid.save(f'{args.dir}/retrieved/{idx}-clip-v{j}.mid')
if args.visualize:
visualize(output, f'{args.dir}/retrieved/{idx}-clip-v{j}.png')
print(f'Accompanied with instrument {melody}. Sampling time: {time.time()-t0} seconds')
if __name__ == '__main__':
parser = ArgumentParser(description='generate infilling completions')
parser.add_argument('dir', help='directory containing an index of MIDI files')
parser.add_argument('--model', type=str, default='',
help='directory containing an anticipatory model checkpoint')
parser.add_argument('-c', '--count', type=int, default=10,
help='number of clips to sample')
parser.add_argument('-m', '--multiplicity', type=int, default=1,
help='number of generations per clip')
parser.add_argument('-p', '--prompt_length', type=int, default=5,
help='length of the prompt (in seconds)')
parser.add_argument('-l', '--clip_length', type=int, default=20,
help='length of the full clip (in seconds)')
parser.add_argument('-a', '--anticipatory', action='store_true',
help='generate anticipatory results')
parser.add_argument('-b', '--baseline', action='store_true',
help='generate autoregressive (baseline) results')
parser.add_argument('-r', '--retrieve', action='store_true',
help='generate the retrieval baseline')
parser.add_argument('-d', '--midis', type=str, default='',
help='directory containing the reference MIDI files (for retrieval)')
parser.add_argument('-v', '--visualize', action='store_true',
help='plot visualizations')
main(parser.parse_args())
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