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osutipe
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Major error fixes

This commit is contained in:
Alexandre 2024-05-28 21:37:08 +02:00
parent f1aebaaaab
commit 0574c4c509
2 changed files with 42 additions and 22 deletions
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3
main.py
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@ -15,7 +15,8 @@ def main():
offset = timing.offset.total_seconds() * 10e3
print(beatmap.audio_filename)
timings, amplitudes, freqs = sound_process.process_song(beatmap.audio_filename, bpm, offset=offset)
timings, amplitudes, freqs = sound_process.process_song(beatmap.audio_filename, int(bpm), offset0=offset, n_iter_2=48)
# NOTE : remove n_iter_2 to map the whole music
beatmap._hit_objects = place.greedy(bpm, offset, timings, amplitudes)
#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, [], [],)]

61
sound_process.py
View File

@ -144,24 +144,26 @@ 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)
song_data = global_data[int(i*step*sample_rate):int((i+1)*step*sample_rate)]
mx = max(song_data)
is_locked = [False for i in range(len(song_data))]
x = int((len(song_data)*threshold)//100)
#print("X = ", x)
if(len(song_data) != 0):
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")
#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 idx in range(len(elements)):
is_locked[elements[idx][0]] = True
for r in range(len(song_data)):
if(is_locked[r] == False):
global_data[r+int(i*step*sample_rate)] = 0
for r in range(len(song_data)):
if(is_locked[r] == False):
global_data[r+int(i*step*sample_rate)] = 0
return global_data
@ -234,6 +236,9 @@ def compress(Zxx):
res = []
def get_freq(song_name, offset, step, songlen, data, display=False):
"""
for a given list of amplitudes, returns the corresponding peak frequencies
"""
fft_list = []
times = []
current_time = offset
@ -269,7 +274,7 @@ def get_freq(song_name, offset, step, songlen, data, display=False):
frequencies[s] = 0
if(display):
plt.plot([t/1000 for t in range(len(data))], frequencies)
plt.plot([offset+t/1000 for t in range(len(data))], frequencies)
plt.grid()
plt.xlabel("Time (s)")
plt.ylabel("Dominant frequency (Hz)")
@ -279,7 +284,7 @@ def get_freq(song_name, offset, step, songlen, data, display=False):
return frequencies
def void_freq(song_name, offset, songlen, increment, minfreq, maxfreq, upperthr, ampthr, ampfreq, ampval, leniency, write, linear, output_file="trimmed.wav"):
def void_freq(song_name, offset, songlen, increment, minfreq, maxfreq, upperthr, ampthr, ampfreq, ampval, leniency, write, linear, is_stereo, output_file="trimmed.wav"):
"""
song_name : string
offset : int
@ -302,12 +307,22 @@ def void_freq(song_name, offset, songlen, increment, minfreq, maxfreq, upperthr,
subprocess.run(["ffmpeg", "-ss", str(offset), "-t", str(songlen+offset), "-i", song_name, "crop.wav"])
sample_rate, global_data = wavfile.read("crop.wav")
sample_rate, raw_global_data = wavfile.read("crop.wav")
blit = int(sample_rate*increment)
global_data = [0 for i in range(len(raw_global_data))]
subprocess.run(["clear"])
subprocess.run(["rm", "crop.wav"])
if(is_stereo):
print("Converting to mono...")
for x in range(len(raw_global_data)):
global_data[x] = raw_global_data[x][0] + raw_global_data[x][1]
else:
global_data = raw_global_data
#print("Blit :", blit)
pfreq = scipy.fft.rfftfreq(blit, 1/sample_rate)
@ -406,10 +421,11 @@ def get_songlen(filename):
retrieves the length of the song in seconds
"""
sample_rate, global_data = wavfile.read(filename)
print("LEN :", len(global_data)/sample_rate)
return (len(global_data)/sample_rate)
def process_song(filename, bpm, offset=0, div_len_factor=1, n_iter_2=-1, threshold=0.5, divisor=4):
def process_song(filename, bpm, offset0=0, div_len_factor=1, n_iter_2=-1, threshold=0.5, divisor=4):
"""
filename : string (name of the song)
offset : int [+] (song mapping will start from this time in seconds, default is 0)
@ -420,21 +436,24 @@ def process_song(filename, bpm, offset=0, div_len_factor=1, n_iter_2=-1, thresho
divisor : int [+] (beat divisor used to snap the notes, default is 4)
"""
offset = offset0/1000
div_len = div_len_factor*60/bpm-0.01
n_iter = n_iter_2
song_len = get_songlen(filename)
if(n_iter == -1):
song_len = get_songlen(filename)
n_iter = int(song_len/div_len)-1/3
n_iter = int((song_len-offset/1000)/div_len)-4
filtered_name = f"{filename}_trimmed.wav"
void_freq(filename, offset, offset+div_len*(n_iter+1)+0.01, 4*60/bpm, minfreq=0, maxfreq=330, upperthr=5000, ampthr=60, ampfreq = 1200, ampval = 5.0, leniency = 0.005, write=True, linear=False, output_file=filtered_name)
void_freq(filename, offset, min(song_len, offset+div_len*(n_iter+1)+0.01), 4*60/bpm, minfreq=0, maxfreq=220, upperthr=5000, ampthr=60, ampfreq = 1200, ampval = 5.0, leniency = 0.005, write=True, linear=False, is_stereo=True, output_file=filtered_name)
#void_freq(filename, offset, offset+div_len*(n_iter+1)+0.01, 4*60/bpm, minfreq=0, maxfreq=330, upperthr=2500, ampthr=60, ampfreq = 1200, ampval = 1/2000, leniency = 0.0, write=True, linear=True, output_file=filtered_name)
datares = filter_n_percent_serial(filtered_name, offset, n_iter, div_len, threshold)
datares = snap(datares, 44100, bpm, 4, True)
frequencies = get_freq(filtered_name, offset, div_len, div_len*n_iter, datares, True)
#Path(f"{filename}_trimmed.wav").unlink()
Path(f"{filename}_trimmed.wav").unlink()
return convert_tuple(datares, frequencies)