mp2i-vms.fr
Experimental Changes
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Thibaud
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ad5049d1ca
commit
d499dee0de
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@ -8,10 +8,9 @@ def compare_plot():
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beatmap = sl.Beatmap.from_path(filename)
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timing = beatmap.timing_points[0]
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bpm = timing.bpm
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offset = timing.offset.total_seconds() * 1000
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data = sound_process.process_song(beatmap.audio_filename, bpm, offset0=offset, n_iter_2=48, divisor=4)
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timings, amplitudes, freqs = [x[0].total_seconds() for x in data], [x[1] for x in data], [x[2] for x in data]
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offset = timing.offset
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timings, amplitudes = sound_process.process_song(beatmap.audio_filename, bpm, offset=offset, n_iter_2=-1)
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timings = [x.total_seconds() for x in timings]
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original_times = [x.time.total_seconds() for x in beatmap.hit_objects(spinners=False) if x.time.total_seconds() <= timings[len(timings) - 1]]
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6
main.py
6
main.py
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@ -10,15 +10,13 @@ def main():
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beatmap = sl.Beatmap.from_path(filename)
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timing = beatmap.timing_points[0]
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bpm = timing.bpm
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offset = timing.offset.total_seconds() * 1000
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offset = timing.offset
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print(beatmap.audio_filename)
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data = sound_process.process_song(beatmap.audio_filename, int(bpm), offset0=offset, n_iter_2=-1)
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timings, amplitudes = sound_process.process_song(beatmap.audio_filename, bpm, offset0=offset, n_iter_2=-1)
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# NOTE : remove n_iter_2 to map the whole music
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timings, amplitudes, freqs = [x[0] for x in data], [x[1] for x in data], [x[2] for x in data]
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beatmap._hit_objects = place.greedy(bpm, offset, timings, amplitudes)
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beatmap.display_name = "TIPE's Extra"
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#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, [], [],)]
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beatmap.write_path("rewrite.osu")
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@ -15,6 +15,8 @@ from pathlib import Path
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from time import sleep
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from datetime import timedelta
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WORKING_SAMPLE_RATE = 1000
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print("Starting...\n")
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def filter_n_percent_serial(song_name, offset, n_iter, step, threshold):
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@ -28,6 +30,8 @@ def filter_n_percent_serial(song_name, offset, n_iter, step, threshold):
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filter data associated with song_name to keep only the highest threshold% values
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"""
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offset = offset.total_seconds()
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subprocess.run(["ffmpeg", "-ss", str(offset), "-t", str(offset+step*n_iter), "-i", song_name, "crop.wav"])
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sample_rate, global_data = wavfile.read('crop.wav')
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@ -94,6 +98,7 @@ def get_freq(song_name, offset, step, songlen, data, display=False):
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"""
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for a given list of amplitudes, returns the corresponding peak frequencies
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"""
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offset = offset.total_seconds()
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fft_list = []
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times = []
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current_time = offset
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@ -118,7 +123,7 @@ def get_freq(song_name, offset, step, songlen, data, display=False):
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for s in range(len(data)):
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if(data[s] != 0):
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pff = scipy.fft.rfft(global_data[int(s*len(global_data)/len(data)):int(44100*step+int(s*len(global_data)/len(data)))])
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pff = scipy.fft.rfft(global_data[int(s*len(global_data)/len(data)):int(WORKING_SAMPLE_RATE*step+int(s*len(global_data)/len(data)))])
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mx = max(np.abs(pff))
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for id in range(len(pff)):
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@ -167,6 +172,8 @@ def void_freq(song_name, offset, songlen, increment, minfreq, maxfreq, upperthr,
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write : bool (should be set to True)
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output_file : technical
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"""
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offset = offset.total_seconds()
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fft_list = []
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times = []
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current_time = offset
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@ -257,7 +264,7 @@ def void_freq(song_name, offset, songlen, increment, minfreq, maxfreq, upperthr,
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res[i] = np.int16(32767*res[i]/mx)
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res = np.array(res)
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wavfile.write(output_file, 44100, res)
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wavfile.write(output_file, WORKING_SAMPLE_RATE, res)
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#plt.plot(np.abs(pfreq[:len(fft_list[0])]), np.abs(fft_list[0]))
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#plt.grid()
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@ -460,21 +467,39 @@ def snap2(data, sample_rate, bpm, first_offset=0, div=4, show=False, adjust=Fals
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return new
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def convert_to_wav(song_name:str, output_file="audio.wav") -> str:
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def convert_song(song_name:str, output_file="audio.wav") -> str:
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"""
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Converts the song to .wav, only if it's not already in wave format.
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Currently relies on file extension.
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Converts the song to .wav AND lower its sample rate to 1000.
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Returns: the song_name that should be used afterwards.
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"""
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extension = Path(song_name).suffix
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match extension:
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case ".mp3" | ".ogg":
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print("Converting to .wav...")
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subprocess.run(["ffmpeg", "-y", "-i", song_name, output_file])
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subprocess.run(["ffmpeg", "-y", "-i", song_name, "-ar", "1000", output_file])
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return output_file
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return song_name
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def process_song(filename, bpm, offset0=0, div_len_factor=1, n_iter_2=-1, threshold=0.5, divisor=4):
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def quantify(time: timedelta, bpm, offset, snapping):
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"""
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Input: timedelta, bpm, offset, and snapping divisor (2 for 1/2, etc...)
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Returns a timedelta that is properly timed to the map.
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"""
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offset_ms = offset.total_seconds() / 1000
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time_ms = time.total_seconds() * 1000
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time_spacing = (60000/bpm)/snapping
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beats_away = round((time_ms - offset_ms)/time_spacing)
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new_time = timedelta(milliseconds=time_spacing*beats_away + offset_ms)
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return new_time
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def quantify_all(amplitudes_ugly, bpm, offset_ms, divisor):
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n = len(amplitudes_ugly)
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covered = [False] * n
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times = []
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amplitudes = []
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for i in range(n):
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if amplitudes_ugly[i] != 0 and not covered[i]:
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times.append(quantify(timedelta(milliseconds=i), bpm, offset_ms, divisor))
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amplitudes.append(amplitudes_ugly[i])
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covered[i] = True
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return times, amplitudes
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def process_song(filename, bpm, offset=timedelta(milliseconds=0), div_len_factor=1, n_iter_2=-1, threshold=0.5, divisor=4):
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"""
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filename : string (name of the song)
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offset : int [+] (song mapping will start from this time in seconds, default is 0)
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@ -485,9 +510,7 @@ def process_song(filename, bpm, offset0=0, div_len_factor=1, n_iter_2=-1, thresh
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divisor : int [+] (beat divisor used to snap the notes, default is 4)
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"""
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filename = convert_to_wav(filename)
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offset = offset0/1000
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filename = convert_song(filename)
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div_len = div_len_factor*60/bpm-0.01
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@ -495,18 +518,19 @@ def process_song(filename, bpm, offset0=0, div_len_factor=1, n_iter_2=-1, thresh
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song_len = get_songlen(filename)
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if(n_iter == -1):
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n_iter = int((song_len-offset/1000)/div_len)-1
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n_iter = floor((song_len-offset.total_seconds())/div_len)-1
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filtered_name = f"{filename}_trimmed.wav"
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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, output_file=filtered_name)
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void_freq(filename, offset, min(song_len, offset.total_seconds()+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, output_file=filtered_name)
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#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)
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datares = filter_n_percent_serial(filtered_name, offset, n_iter, div_len, threshold)
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#datares = snap(datares, 44100, bpm, 4, True)
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datares = snap2(datares, 44100, bpm, first_offset=offset, div=divisor, show=True, adjust=True)
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frequencies = get_freq(filtered_name, offset, div_len, div_len*n_iter, datares, True)
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amplitudes_ugly = filter_n_percent_serial(filtered_name, offset, n_iter, div_len, threshold)
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#datares = snap(datares, WORKING_SAMPLE_RATE, bpm, 4, True)
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times, amplitudes = quantify_all(amplitudes_ugly, bpm, offset, divisor)
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#frequencies = get_freq(filtered_name, offset, div_len, div_len*n_iter, datares, True)
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Path(f"{filename}_trimmed.wav").unlink()
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return convert_tuple(datares, frequencies)
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#return convert_tuple(datares, frequencies)
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return times, amplitudes
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def main():
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data = process_song("tetris_4.wav", 160, n_iter_2=48, threshold=100)
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@ -564,7 +588,7 @@ if(False):
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#t, f, Zxx = fct("deltamax.wav", 9.992, 0.032, 20, 3000, False)
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#t, f, Zxx = fct("da^9.wav", 8.463, 0.032, 20, 5000, False)
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t, f, Zxx = fct("13. Cosmic Mind.wav", 0, 0.032, 20, 5000, False)
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#t, f, Zxx = fct("Furioso Melodia 44100.wav", 4, 0.032, 8, 3000, False)
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#t, f, Zxx = fct("Furioso Melodia WORKING_SAMPLE_RATE.wav", 4, 0.032, 8, 3000, False)
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#t, f, Zxx = fct("changing.wav", 0, 0.05, 3.9, 5000, False)
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#fct("worlds_end_3.wav", 75, (60/178)/4, 75+2, 2500)
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@ -575,7 +599,7 @@ if(False):
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(t, data) = peaks("worlds_end_3.wav", 74.582, 6, False, 0.9)
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#(t, data) = peaks("da^9.wav", 8.463, 301.924 - 8.463, False, 0.95)
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#(t, data) = peaks("deltamax.wav", 8.463, 30101.924 - 8.463, False, 0.92)
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da = find_bpm(t, 44100, data, 100, 200, 1, 10)
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da = find_bpm(t, WORKING_SAMPLE_RATE, data, 100, 200, 1, 10)
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print("BPM data is", da)'''
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#data = [-1 for i in range(int(x))]
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@ -777,6 +801,7 @@ def extract_peaks_v2(song_data, sample_rate, offset, display, threshold, seglen)
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return (t, song_data)
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def peaks(song_name, offset, length, display, thr):
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offset = offset.total_seconds()
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subprocess.run(["ffmpeg", "-ss", str(offset), "-t", str(length), "-i", song_name, "crop.wav"])
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sample_rate, audio_data = wavfile.read('crop.wav')
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@ -785,7 +810,7 @@ def peaks(song_name, offset, length, display, thr):
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subprocess.run(["rm", "crop.wav"])
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#return extract_peaks(audio_data, sample_rate, offset, display, thr)
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return extract_peaks_v2(audio_data, sample_rate, offset, display, thr, 44100*2)
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return extract_peaks_v2(audio_data, sample_rate, offset, display, thr, WORKING_SAMPLE_RATE*2)
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def find_bpm(sample_rate, data, minbpm, maxbpm, step, width):
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optimal = minbpm
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@ -987,6 +1012,8 @@ def filter_n_percent(song_name, offset, length, threshold, reduce, show):
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# threshold is in ]0, 100]
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# filter data associated with song_name to keep only the highest threshold% values
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offset = offset.total_seconds()
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subprocess.run(["ffmpeg", "-ss", str(offset), "-t", str(length), "-i", song_name, "crop.wav"])
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sample_rate, song_data = wavfile.read('crop.wav')
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@ -995,7 +1022,7 @@ def filter_n_percent(song_name, offset, length, threshold, reduce, show):
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subprocess.run(["rm", "crop.wav"])
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if(reduce):
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(song_data,e) = to_ms(song_data, 44100, 1)
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(song_data,e) = to_ms(song_data, WORKING_SAMPLE_RATE, 1)
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sample_rate = 1000
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mx = max(song_data)
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