def spectrogram(song, precision=2, fmax=10000, fmin=0, n_mels=128):
meta = load_metadata(song)
bpm = meta['_beatsPerMinute']
clip, sample_rate = librosa.load(path_to_song(song), None)
clip_length = len(clip) / sample_rate
# 1 beat = quarter note
samples_per_beat = 60 * sample_rate / bpm
samples_per_segment = samples_per_beat / precision # to get 1/32
print("bpm=", bpm)
print("precision=", precision)
print("clip_length=", clip_length)
print("samples_per_beat=", samples_per_beat)
print("samples_per_segment=", samples_per_segment)
mel_spec = librosa.feature.melspectrogram(clip, n_fft=10240, hop_length=int(samples_per_segment), sr=sample_rate,
fmin=fmin, fmax=fmax, n_mels=n_mels)
print(mel_spec.shape)
# librosa.display.specshow(librosa.amplitude_to_db(mel_spec, ref=np.max),
# y_axis='log', x_axis='time', sr=sample_rate)
# plt.title('melspectrogram')
# plt.colorbar(format='%+2.0f dB')
# plt.tight_layout()
# plt.show()
return mel_spec
def beatmap2onset(song, difficulty='expert', precision=2):
metadata = load_metadata(song)
beatmap = load_beatmap(song, difficulty)
notes = list(reversed(sorted(beatmap['_notes'], key=lambda x: x['_time'])))
bpm = metadata['_beatsPerMinute']
one_beat_length = 60 / bpm # len of one beat in seconds
one_segment_length = one_beat_length / precision # to get 1/32
print('track=', metadata['_songAuthorName'], metadata['_songName'])
print('bpm=', bpm)
print('last_note(beats)=', notes[0]['_time'])
print('time(sec)=', notes[0]['_time'] / bpm * 60)
print('one_beat_length(sec)=', one_beat_length)
print('one_segment_length(sec)=', one_segment_length)
print('segments_in_song=',
(notes[0]['_time'] / bpm * 60) / one_segment_length)
print('notes=', len(notes))
segments = []
end_in_secs = one_segment_length
def to_secs(beats):
return beats / bpm * 60
# we loop until we remove all notes from the collection
while len(notes) != 0:
has = False
# we take all the notes from the end of notes list (because we reversed the list
# to allow us to pop() from end) that occur inside this segment (start, end]
while len(notes) != 0 and to_secs(notes[-1]['_time']) < end_in_secs:
note = notes.pop()
has = True
segments.append(1.0 if has else 0.0)
# we go to next segment
end_in_secs += one_segment_length
print('segments=', len(segments))
H = np.array(segments)
print('H.shape', H.shape)
return H, bpm, 0, 0, len(segments)
from functions import load_metadata
os.makedirs('./beatmaps', exist_ok=True)
os.makedirs('./beatmaps/easy', exist_ok=True)
os.makedirs('./beatmaps/normal', exist_ok=True)
os.makedirs('./beatmaps/hard', exist_ok=True)
os.makedirs('./beatmaps/expert', exist_ok=True)
os.makedirs('./beatmaps/expertplus', exist_ok=True)
os.makedirs('./beatmaps/metadata', exist_ok=True)
os.makedirs('./beatmaps/ogg', exist_ok=True)
for f in os.listdir('./songs'):
# metadata
os.rename(f'./songs/{f}/info.dat', f'./beatmaps/metadata/{f}.json')
metadata = load_metadata(f)
# song
song_name = metadata['_songFilename']
os.rename(f'./songs/{f}/{song_name}', f'./beatmaps/ogg/{f}.ogg')
# beatmaps
for d in ['Easy', 'Normal', 'Hard', 'Expert', 'ExpertPlus']:
if os.path.exists(f'./songs/{f}/{d}.dat'):
os.rename(f'./songs/{f}/{d}.dat',
f'./beatmaps/{d.lower()}/{f}.json')
print('easy maps=', len(os.listdir('./beatmaps/easy')))
print('normal maps=', len(os.listdir('./beatmaps/normal')))
print('hard maps=', len(os.listdir('./beatmaps/hard')))
print('expert maps=', len(os.listdir('./beatmaps/expert')))
def beatmap2f24(song, difficulty='expert', precision=2):
metadata = load_metadata(song)
beatmap = load_beatmap(song, difficulty)
notes = list(reversed(sorted(beatmap['_notes'], key=lambda x: x['_time'])))
bpm = metadata['_beatsPerMinute']
one_beat_length = 60 / bpm # len of one beat in seconds
one_segment_length = one_beat_length / precision # to get 1/32
print('track=', metadata['_songAuthorName'], metadata['_songName'])
print('bpm=', bpm)
print('last_note(beats)=', notes[0]['_time'])
print('time(sec)=', notes[0]['_time'] / bpm * 60)
print('one_beat_length(sec)=', one_beat_length)
print('one_segment_length(sec)=', one_segment_length)
print('segments_in_song=',
(notes[0]['_time'] / bpm * 60) / one_segment_length)
print('notes=', len(notes))
segments = []
empty_segments = 0
collision_count = 0
end_in_secs = one_segment_length
def to_secs(beats):
return beats / bpm * 60
# we loop until we remove all notes from the collection
while len(notes) != 0:
segment = [0.0 for _ in range(24)]
empt = True
# we take all the notes from the end of notes list (because we reversed the list
# to allow us to pop() from end) that occur inside this segment (start, end]
while len(notes) != 0 and to_secs(notes[-1]['_time']) < end_in_secs:
note = notes.pop()
idx = note['_lineIndex'] + note['_lineLayer'] * 3
if note['_type'] == 1:
idx += 12
if segment[idx] > 0.0:
collision_count += 1
segment[idx] = 1.0
empt = False
segments.append(segment)
if empt:
empty_segments += 1
# we go to next segment
end_in_secs += one_segment_length
print('segments=', len(segments))
print('empty segments=', empty_segments,
100 * empty_segments / len(segments))
print('collisions=', collision_count,
100 * collision_count / len(segments))
H = np.array(segments)
print('H.shape', H.shape)
# plt.imshow(H, aspect='auto', interpolation='sinc', vmin=0, vmax=0.03)
# plt.show()
# np.save('bsmap', H)
return H, bpm, empty_segments, collision_count, len(segments)
from functions import list_songs, load_metadata
non_zero = 0
zero = 0
for x in list_songs('expert'):
meta = load_metadata(x)
if meta['_songTimeOffset'] != 0:
non_zero += 1
else:
zero += 1
print('zero=', zero)
print('non_zero=', non_zero)