def beat_extractor(queue_beat):
kwargs = dict(
fps=100,
correct=True,
infile=None,
outfile=None,
max_bpm=170,
min_bpm=60,
#nn_files = [BEATS_LSTM[0]],
transition_lambda=100,
num_frames=1,
online=True,
verbose=1)
def beat_callback(beats, output=None):
if len(beats) > 0:
# Do something with the beat (for now, just print the array to stdout)
queue_beat.put(beats[0])
#print(beats)
#print('Process to write betas: %s' % os.getpid())
in_processor = RNNBeatProcessor(**kwargs)
beat_processor = DBNBeatTrackingProcessor(**kwargs)
out_processor = [beat_processor, beat_callback]
processor = IOProcessor(in_processor, out_processor)
process_online(processor, **kwargs)
"Otherwise the model's performance will be very poor.\n")
# initialize network
print("Initializing tagging network ...")
model = select_model(args.model)
net = model.build_model(batch_size=1)
# initialize neural network
TAGGER = Network(net, print_architecture=False)
# load model parameters network
if args.params is not None:
dump_file = args.params
else:
out_path = os.path.join(os.path.join(EXP_ROOT), model.EXP_NAME)
dump_file, log_file = get_dump_file_paths(out_path, 1)
dump_file = dump_file.replace(".pkl", "_it0.pkl")
print("Loading model from: %s" % dump_file)
TAGGER.load(dump_file)
# set prediction rate
PREDICT_EVERY_K = args.predict_every_k
# dummy prediction to compile model
print("Compiling prediction function ...")
TAGGER.predict(SLIDING_WINDOW[np.newaxis, np.newaxis])
print("Starting prediction loop ...")
processor = IOProcessor(in_processor=processor_pipeline2, out_processor=output_processor)
process_online(processor, infile=None, outfile=None, sample_rate=32000)