def transposition(events, controls, offset=0):
# events [steps, batch_size, event_dim]
# return events, controls
events = np.array(events, dtype=np.int64)
controls = np.array(controls, dtype=np.float32)
event_feat_ranges = EventSeq.feat_ranges()
on = event_feat_ranges['note_on']
off = event_feat_ranges['note_off']
if offset > 0:
indeces0 = (((on.start <= events) & (events < on.stop - offset)) |
((off.start <= events) & (events < off.stop - offset)))
indeces1 = (((on.stop - offset <= events) & (events < on.stop)) |
((off.stop - offset <= events) & (events < off.stop)))
events[indeces0] += offset
events[indeces1] += offset - 12
elif offset < 0:
indeces0 = (((on.start - offset <= events) & (events < on.stop)) |
((off.start - offset <= events) & (events < off.stop)))
indeces1 = (((on.start <= events) & (events < on.start - offset)) |
((off.start <= events) & (events < off.start - offset)))
events[indeces0] += offset
events[indeces1] += offset + 12
assert ((0 <= events) & (events < EventSeq.dim())).all()
histr = ControlSeq.feat_ranges()['pitch_histogram']
controls[:, :, histr.start:histr.stop] = np.roll(
controls[:, :, histr.start:histr.stop], offset, -1)
return events, controls
cnt_dict['index-' + str(index)] = 1
return cnt_arr
print(par.vocab_size)
data = Data('dataset/processed')
# ds = DataSequence('dataset/processed', 10, 2048)
sample = data.seq2seq_batch(1000, 100)[0]
pprint.pprint(list(sample))
arr = count_dict(par.vocab_size + 3, sample)
pprint.pprint(arr)
from sequence import EventSeq, Event
event_cnt = {'note_on': 0, 'note_off': 0, 'velocity': 0, 'time_shift': 0}
for event_index in range(len(arr)):
for event_type, feat_range in EventSeq.feat_ranges().items():
if feat_range.start <= event_index < feat_range.stop:
print(event_type + ':' + str(arr[event_index]) +
' event cnt: ' + str(event_cnt))
event_cnt[event_type] += arr[event_index]
# event_value = event_index - feat_range.start
# events.append(Event(event_type, time, event_value))
# if event_type == 'time_shift':
# time += EventSeq.time_shift_bins[event_value]
# break
print(event_cnt)
# print(np.max(sample), np.min(sample))
# print([data._get_seq(file).shape for file in data.files])