import PIL
import numpy as np
class MIDITokenizer:
def __init__(self):
self.vocab_size = 0
def allocate_ids(size):
ids = [self.vocab_size + i for i in range(size)]
self.vocab_size += size
return ids
self.pad_id = allocate_ids(1)[0]
self.bos_id = allocate_ids(1)[0]
self.eos_id = allocate_ids(1)[0]
self.events = {
"note": ["time1", "time2", "track", "duration", "channel", "pitch", "velocity"],
"patch_change": ["time1", "time2", "track", "channel", "patch"],
"control_change": ["time1", "time2", "track", "channel", "controller", "value"],
"set_tempo": ["time1", "time2", "track", "bpm"],
}
self.event_parameters = {
"time1": 128, "time2": 16, "duration": 2048, "track": 128, "channel": 16, "pitch": 128, "velocity": 128,
"patch": 128, "controller": 128, "value": 128, "bpm": 256
}
self.event_ids = {e: allocate_ids(1)[0] for e in self.events.keys()}
self.id_events = {i: e for e, i in self.event_ids.items()}
self.parameter_ids = {p: allocate_ids(s) for p, s in self.event_parameters.items()}
self.max_token_seq = max([len(ps) for ps in self.events.values()]) + 1
def tempo2bpm(self, tempo):
tempo = tempo / 10 ** 6 # us to s
bpm = 60 / tempo
return bpm
def bpm2tempo(self, bpm):
if bpm == 0:
bpm = 1
tempo = int((60 / bpm) * 10 ** 6)
return tempo
def tokenize(self, midi_score, add_bos_eos=True):
ticks_per_beat = midi_score[0]
event_list = {}
track_num = len(midi_score[1:])
for track_idx, track in enumerate(midi_score[1:129]):
for event in track:
t = round(16 * event[1] / ticks_per_beat)
new_event = [event[0], t // 16, t % 16, track_idx] + event[2:]
if event[0] == "note":
new_event[4] = max(1, round(16 * new_event[4] / ticks_per_beat))
elif event[0] == "set_tempo":
new_event[4] = int(self.tempo2bpm(new_event[4]))
key = hash(tuple(new_event[:-1]))
event_list[key] = new_event
event_list = list(event_list.values())
event_list = sorted(event_list, key=lambda e: (e[1] * 16 + e[2]) * track_num + e[3])
midi_seq = []
last_t1 = 0
for event in event_list:
name = event[0]
if name in self.event_ids:
params = event[1:]
cur_t1 = params[0]
params[0] = params[0] - last_t1
if not all([0 <= params[i] < self.event_parameters[p] for i, p in enumerate(self.events[name])]):
continue
tokens = [self.event_ids[name]] + [self.parameter_ids[p][params[i]]
for i, p in enumerate(self.events[name])]
tokens += [self.pad_id] * (self.max_token_seq - len(tokens))
midi_seq.append(tokens)
last_t1 = cur_t1
if add_bos_eos:
bos = [self.bos_id] + [self.pad_id] * (self.max_token_seq - 1)
eos = [self.eos_id] + [self.pad_id] * (self.max_token_seq - 1)
midi_seq = [bos] + midi_seq + [eos]
return midi_seq
def event2tokens(self, event):
name = event[0]
params = event[1:]
tokens = [self.event_ids[name]] + [self.parameter_ids[p][params[i]]
for i, p in enumerate(self.events[name])]
tokens += [self.pad_id] * (self.max_token_seq - len(tokens))
return tokens
def detokenize(self, midi_seq):
ticks_per_beat = 480
tracks_dict = {}
t1 = 0
for tokens in midi_seq:
if tokens[0] in self.id_events:
name = self.id_events[tokens[0]]
if len(tokens) <= len(self.events[name]):
continue
params = tokens[1:]
params = [params[i] - self.parameter_ids[p][0] for i, p in enumerate(self.events[name])]
if not all([0 <= params[i] < self.event_parameters[p] for i, p in enumerate(self.events[name])]):
continue
event = [name] + params
if name == "set_tempo":
event[4] = self.bpm2tempo(event[4])
if event[0] == "note":
event[4] = int(event[4] * ticks_per_beat / 16)
t1 += event[1]
t = t1 * 16 + event[2]
t = int(t * ticks_per_beat / 16)
track_idx = event[3]
if track_idx not in tracks_dict:
tracks_dict[track_idx] = []
tracks_dict[track_idx].append([event[0], t] + event[4:])
tracks = list(tracks_dict.values())
for i in range(len(tracks)):
track = tracks[i]
track = sorted(track, key=lambda e: e[1])
last_note_t = {}
for e in reversed(track):
if e[0] == "note":
t, d, c, p = e[1:5]
key = (c, p)
if key in last_note_t:
d = min(d, max(last_note_t[key] - t, 0)) # to avoid note overlap
last_note_t[key] = t
e[2] = d
tracks[i] = track
return [ticks_per_beat, *tracks]
def midi2img(self, midi_score):
ticks_per_beat = midi_score[0]
notes = []
max_time = 1
track_num = len(midi_score[1:])
for track_idx, track in enumerate(midi_score[1:]):
for event in track:
t = round(16 * event[1] / ticks_per_beat)
if event[0] == "note":
d = max(1, round(16 * event[2] / ticks_per_beat))
c, p = event[3:5]
max_time = max(max_time, t + d + 1)
notes.append((track_idx, c, p, t, d))
img = np.zeros((128, max_time, 3), dtype=np.uint8)
colors = {(i, j): np.random.randint(50, 256, 3) for i in range(track_num) for j in range(16)}
for note in notes:
tr, c, p, t, d = note
img[p, t: t + d] = colors[(tr, c)]
img = PIL.Image.fromarray(np.flip(img, 0))
return img