mp2i-vms.fr
Tried fixing snap issues (although it's snapped, but not in the right way I guess)
This commit is contained in:
parent
e2e207c294
commit
b957068977
4
main.py
4
main.py
|
|
@ -13,12 +13,12 @@ def main():
|
|||
offset = timing.offset.total_seconds() * 1000
|
||||
print(beatmap.audio_filename)
|
||||
|
||||
data = sound_process.process_song(beatmap.audio_filename, int(bpm), offset0=offset, n_iter_2=48)
|
||||
data = sound_process.process_song(beatmap.audio_filename, int(bpm), offset0=offset, n_iter_2=32)
|
||||
# NOTE : remove n_iter_2 to map the whole music
|
||||
timings, amplitudes, freqs = [x[0] for x in data], [x[1] for x in data], [x[2] for x in data]
|
||||
|
||||
beatmap._hit_objects = place.greedy(bpm, offset, timings, amplitudes)
|
||||
beatmap.display_name = "TIPE's Extra"
|
||||
#beatmap.display_name = "TIPE's Extra"
|
||||
#beatmap._hit_objects = [sl.Slider(sl.Position(0, 0), timedelta(milliseconds=3), timedelta(milliseconds=130), 0, sl.curve.Linear([sl.Position(0, 0), sl.Position(100, 100)], 100), 100, 2, 1, 1, 1, timing.ms_per_beat, [], [],)]
|
||||
beatmap.write_path("rewrite.osu")
|
||||
|
||||
|
|
|
|||
240
sound_process.py
240
sound_process.py
|
|
@ -17,114 +17,6 @@ from datetime import timedelta
|
|||
|
||||
print("Starting...\n")
|
||||
|
||||
def find_bpm_2(sample_rate, data, threshold, maxbpm, show):
|
||||
mx = np.max(data)
|
||||
min_spacing = (60*sample_rate)/maxbpm
|
||||
k = 0
|
||||
while(k < len(data) and data[k]/mx < threshold):
|
||||
k += 1
|
||||
|
||||
k += 1
|
||||
spacing = []
|
||||
current = 1
|
||||
progress = 0
|
||||
|
||||
while(k < len(data)):
|
||||
if(k%(len(data)/100) == 0):
|
||||
print(progress, "%")
|
||||
progress += 1
|
||||
if(data[k]/mx >= threshold and current > min_spacing):
|
||||
spacing.append(current)
|
||||
current = 0
|
||||
else:
|
||||
current += 1
|
||||
k += 1
|
||||
|
||||
|
||||
for x in range(len(spacing)):
|
||||
spacing[x] = 60/(spacing[x]/sample_rate)
|
||||
|
||||
digits = [i for i in range(len(spacing))]
|
||||
if(show):
|
||||
plt.plot(digits, spacing)
|
||||
plt.xlabel("N")
|
||||
plt.ylabel("BPM")
|
||||
plt.grid()
|
||||
plt.show()
|
||||
|
||||
beat = np.mean(spacing)
|
||||
error = np.std(spacing)
|
||||
|
||||
return (np.round(beat, 3), np.round(error, 3))
|
||||
|
||||
def to_ms(song_data, sample_rate, offset):
|
||||
# converts audio data to have exactly 1 sample per millisecond (aka set sample_rate to 1000)
|
||||
new_data = []
|
||||
spacing = int(sample_rate * 0.001)
|
||||
mx = max(song_data)
|
||||
i = 0
|
||||
while(i < len(song_data)):
|
||||
avg = 0
|
||||
for k in range(spacing):
|
||||
if(i+spacing < len(song_data)):
|
||||
avg += song_data[i+spacing]
|
||||
avg = avg / spacing
|
||||
new_data.append(avg)
|
||||
i += spacing
|
||||
|
||||
if(False): # pls dont kill me thx
|
||||
t = [offset + j/1000 for j in range(len(new_data))]
|
||||
plt.plot(t, new_data)
|
||||
plt.xlabel("Time")
|
||||
plt.ylabel("Amplitude")
|
||||
plt.grid()
|
||||
plt.show()
|
||||
|
||||
return (new_data, len(new_data))
|
||||
|
||||
def filter_n_percent(song_name, offset, length, threshold, reduce, show):
|
||||
# threshold is in ]0, 100]
|
||||
# filter data associated with song_name to keep only the highest threshold% values
|
||||
|
||||
subprocess.run(["ffmpeg", "-ss", str(offset), "-t", str(length), "-i", song_name, "crop.wav"])
|
||||
|
||||
sample_rate, song_data = wavfile.read('crop.wav')
|
||||
|
||||
subprocess.run(["clear"])
|
||||
subprocess.run(["rm", "crop.wav"])
|
||||
|
||||
if(reduce):
|
||||
(song_data,e) = to_ms(song_data, 44100, 1)
|
||||
sample_rate = 1000
|
||||
|
||||
mx = max(song_data)
|
||||
|
||||
is_locked = [False for i in range(len(song_data))]
|
||||
x = int((len(song_data)*threshold)//100)
|
||||
#print("X = ", x)
|
||||
|
||||
print("Retreiving the", int(x), "/", len(song_data), "highest values")
|
||||
elements = heapq.nlargest(int(x), enumerate(song_data), key=lambda x: x[1])
|
||||
print("Done")
|
||||
|
||||
for idx in range(len(elements)):
|
||||
is_locked[elements[idx][0]] = True
|
||||
|
||||
for r in range(len(song_data)):
|
||||
if(is_locked[r] == False):
|
||||
song_data[r] = 0
|
||||
|
||||
if(show):
|
||||
#print("EEEEE")
|
||||
t = [offset + j/sample_rate for j in range(len(song_data))]
|
||||
plt.plot(t, song_data)
|
||||
plt.xlabel("Time")
|
||||
plt.ylabel("Amplitude")
|
||||
plt.grid()
|
||||
plt.show()
|
||||
|
||||
return song_data
|
||||
|
||||
def filter_n_percent_serial(song_name, offset, n_iter, step, threshold):
|
||||
"""
|
||||
song_name : string
|
||||
|
|
@ -145,7 +37,7 @@ def filter_n_percent_serial(song_name, offset, n_iter, step, threshold):
|
|||
|
||||
for i in range(n_iter):
|
||||
print(i, "/", n_iter)
|
||||
print(i * step)
|
||||
#print(i * step)
|
||||
song_data = global_data[int(i*step*sample_rate):int((i+1)*step*sample_rate)]
|
||||
|
||||
if(len(song_data) != 0):
|
||||
|
|
@ -483,8 +375,12 @@ def snap2(data, sample_rate, bpm, first_offset=0, div=4, show=False):
|
|||
print("Snap done")
|
||||
|
||||
if(show):
|
||||
new2 = [0.9 if new[i] != 0 else 0 for i in range(len(new))]
|
||||
|
||||
t = [j/1000+first_offset for j in range(len(new))]
|
||||
beats = [0 for j in range(len(new))]
|
||||
beats_1 = [0 for j in range(len(new))]
|
||||
beats_2 = [0 for k in range(len(new))]
|
||||
beats_4 = [0 for l in range(len(new))]
|
||||
|
||||
k = 0
|
||||
current_t = first_offset
|
||||
|
|
@ -494,13 +390,21 @@ def snap2(data, sample_rate, bpm, first_offset=0, div=4, show=False):
|
|||
current_t = first_offset + k*60/(bpm*div)
|
||||
|
||||
while(1000*current_t < len(new)):
|
||||
beats[int(1000*current_t)] = 16384
|
||||
beats_4[int(1000*current_t)] = 0.9
|
||||
|
||||
if(k % 2 == 0):
|
||||
beats_2[int(1000*current_t)] = 0.92
|
||||
|
||||
if(k % 4 == 0):
|
||||
beats_1[int(1000*current_t)] = 0.94
|
||||
|
||||
k += 1
|
||||
current_t = first_offset + k*60/(bpm*div)
|
||||
|
||||
plt.plot(t, beats)
|
||||
plt.plot(t, new)
|
||||
plt.plot(t, new2, "bo")
|
||||
plt.plot(t, beats_4, "r-")
|
||||
plt.plot(t, beats_2, "y-")
|
||||
plt.plot(t, beats_1, "g-")
|
||||
plt.xlabel("Time (s)")
|
||||
plt.ylabel("Amplitude")
|
||||
plt.grid()
|
||||
|
|
@ -557,7 +461,7 @@ def process_song(filename, bpm, offset0=0, div_len_factor=1, n_iter_2=-1, thresh
|
|||
return convert_tuple(datares, frequencies)
|
||||
|
||||
def main():
|
||||
data = process_song("tetris_4.wav", 160, n_iter_2 = 32)
|
||||
data = process_song("tetris_4.wav", 160, n_iter_2=48, threshold=100)
|
||||
#print(data)
|
||||
print("Program finished with return 0")
|
||||
|
||||
|
|
@ -965,4 +869,112 @@ def void_freq(song_name, offset, songlen, increment, lthr, gthr):
|
|||
wavfile.write('test.wav', sample_rate, np.array(audio_signal, dtype=np.int16))
|
||||
|
||||
print("Done")
|
||||
|
||||
def find_bpm_2(sample_rate, data, threshold, maxbpm, show):
|
||||
mx = np.max(data)
|
||||
min_spacing = (60*sample_rate)/maxbpm
|
||||
k = 0
|
||||
while(k < len(data) and data[k]/mx < threshold):
|
||||
k += 1
|
||||
|
||||
k += 1
|
||||
spacing = []
|
||||
current = 1
|
||||
progress = 0
|
||||
|
||||
while(k < len(data)):
|
||||
if(k%(len(data)/100) == 0):
|
||||
print(progress, "%")
|
||||
progress += 1
|
||||
if(data[k]/mx >= threshold and current > min_spacing):
|
||||
spacing.append(current)
|
||||
current = 0
|
||||
else:
|
||||
current += 1
|
||||
k += 1
|
||||
|
||||
|
||||
for x in range(len(spacing)):
|
||||
spacing[x] = 60/(spacing[x]/sample_rate)
|
||||
|
||||
digits = [i for i in range(len(spacing))]
|
||||
if(show):
|
||||
plt.plot(digits, spacing)
|
||||
plt.xlabel("N")
|
||||
plt.ylabel("BPM")
|
||||
plt.grid()
|
||||
plt.show()
|
||||
|
||||
beat = np.mean(spacing)
|
||||
error = np.std(spacing)
|
||||
|
||||
return (np.round(beat, 3), np.round(error, 3))
|
||||
|
||||
def to_ms(song_data, sample_rate, offset):
|
||||
# converts audio data to have exactly 1 sample per millisecond (aka set sample_rate to 1000)
|
||||
new_data = []
|
||||
spacing = int(sample_rate * 0.001)
|
||||
mx = max(song_data)
|
||||
i = 0
|
||||
while(i < len(song_data)):
|
||||
avg = 0
|
||||
for k in range(spacing):
|
||||
if(i+spacing < len(song_data)):
|
||||
avg += song_data[i+spacing]
|
||||
avg = avg / spacing
|
||||
new_data.append(avg)
|
||||
i += spacing
|
||||
|
||||
if(False): # pls dont kill me thx
|
||||
t = [offset + j/1000 for j in range(len(new_data))]
|
||||
plt.plot(t, new_data)
|
||||
plt.xlabel("Time")
|
||||
plt.ylabel("Amplitude")
|
||||
plt.grid()
|
||||
plt.show()
|
||||
|
||||
return (new_data, len(new_data))
|
||||
|
||||
def filter_n_percent(song_name, offset, length, threshold, reduce, show):
|
||||
# threshold is in ]0, 100]
|
||||
# filter data associated with song_name to keep only the highest threshold% values
|
||||
|
||||
subprocess.run(["ffmpeg", "-ss", str(offset), "-t", str(length), "-i", song_name, "crop.wav"])
|
||||
|
||||
sample_rate, song_data = wavfile.read('crop.wav')
|
||||
|
||||
subprocess.run(["clear"])
|
||||
subprocess.run(["rm", "crop.wav"])
|
||||
|
||||
if(reduce):
|
||||
(song_data,e) = to_ms(song_data, 44100, 1)
|
||||
sample_rate = 1000
|
||||
|
||||
mx = max(song_data)
|
||||
|
||||
is_locked = [False for i in range(len(song_data))]
|
||||
x = int((len(song_data)*threshold)//100)
|
||||
#print("X = ", x)
|
||||
|
||||
print("Retreiving the", int(x), "/", len(song_data), "highest values")
|
||||
elements = heapq.nlargest(int(x), enumerate(song_data), key=lambda x: x[1])
|
||||
print("Done")
|
||||
|
||||
for idx in range(len(elements)):
|
||||
is_locked[elements[idx][0]] = True
|
||||
|
||||
for r in range(len(song_data)):
|
||||
if(is_locked[r] == False):
|
||||
song_data[r] = 0
|
||||
|
||||
if(show):
|
||||
#print("EEEEE")
|
||||
t = [offset + j/sample_rate for j in range(len(song_data))]
|
||||
plt.plot(t, song_data)
|
||||
plt.xlabel("Time")
|
||||
plt.ylabel("Amplitude")
|
||||
plt.grid()
|
||||
plt.show()
|
||||
|
||||
return song_data
|
||||
'''
|
||||
Loading…
Reference in New Issue