| import os, csv, time
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| from argparse import ArgumentParser
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| import numpy as np
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| import torch
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| from transformers import AutoModelForCausalLM
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| from anticipation import ops
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| from anticipation.visuals import visualize
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| from anticipation.convert import midi_to_interarrival, interarrival_to_midi
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| from anticipation.convert import midi_to_events, events_to_midi
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| from anticipation.vocab import MIDI_SEPARATOR,MIDI_START_OFFSET,MIDI_END_OFFSET
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| def main(args):
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| np.random.seed(args.seed)
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| print(f'Prompting using model checkpoint: {args.model}')
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| t0 = time.time()
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| model = AutoModelForCausalLM.from_pretrained(args.model).cuda()
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| print(f'Loaded model ({time.time()-t0} seconds)')
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| print(f'Writing outputs to {args.dir}/{args.output}')
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| try:
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| os.makedirs(f'{args.dir}/{args.output}')
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| except FileExistsError:
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| pass
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| print(f'Prompting with tracks in index : {args.dir}/index.csv')
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| with open(f'{args.dir}/index.csv', newline='') as f:
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| reader = csv.reader(f)
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| header = next(reader)
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| for row in reader:
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| prompt_midi = row[header.index('prompt')]
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| idx = int(row[header.index('idx')])
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| prompt = midi_to_interarrival(os.path.join(args.dir, prompt_midi))
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| max_idx = 0
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| for i,token in enumerate(prompt):
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| if MIDI_START_OFFSET <= token < MIDI_START_OFFSET + MIDI_END_OFFSET:
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| max_idx = i
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| prompt = prompt[:max_idx+1]
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| for j in range(args.multiplicity):
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| t0 = time.time()
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| input_ids = torch.tensor([prompt]).cuda()
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| output = model.generate(input_ids, do_sample=True, max_length=1024, top_p=0.95, pad_token_id=MIDI_SEPARATOR)
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| output = output[0].cpu().tolist()
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| mid = interarrival_to_midi(output)
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| events = midi_to_events(mid)
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| output = ops.clip(events, 0, args.clip_length)
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| mid = events_to_midi(output)
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| mid.save(f'{args.dir}/{args.output}/{idx}-clip-v{j}.mid')
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| if args.visualize:
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| visualize(output, f'{args.dir}/{args.output}/{idx}-clip-v{j}.png')
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| print(f'Generated completion. Sampling time: {time.time()-t0} seconds')
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| if __name__ == '__main__':
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| parser = ArgumentParser(description='generate prompted completions with an interarrival-time model')
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| parser.add_argument('dir', help='directory containing an index of MIDI files')
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| parser.add_argument('model', help='directory containing an interarrival model checkpoint')
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| parser.add_argument('-o', '--output', type=str, default='model',
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| help='model description (the name of the output subdirectory)')
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| parser.add_argument('-s', '--seed', type=int, default=0,
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| help='random seed')
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| parser.add_argument('-c', '--count', type=int, default=10,
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| help='number of clips to sample')
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| parser.add_argument('-m', '--multiplicity', type=int, default=1,
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| help='number of generations per clip')
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| parser.add_argument('-l', '--clip_length', type=int, default=20,
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| help='length of the full clip (in seconds)')
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| parser.add_argument('-v', '--visualize', action='store_true',
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| help='plot visualizations')
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| main(parser.parse_args())
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|
