mp2i-vms.fr
Merge branch 'main' into exporting
This commit is contained in:
Thibaud
commit
a4fb003165
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@ -1,2 +1,3 @@
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*.osu
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*.csv
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.venv
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@ -8,6 +8,11 @@ import wave as wv
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import struct
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import librosa
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import heapq
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import scipy
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import os
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import random
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from pathlib import Path
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from time import sleep
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print("Starting...\n")
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@ -52,6 +57,7 @@ def find_bpm_2(sample_rate, data, threshold, maxbpm, show):
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return (np.round(beat, 3), np.round(error, 3))
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def to_ms(song_data, sample_rate, offset):
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# converts audio data to have exactly 1 sample per millisecond (aka set sample_rate to 1000)
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new_data = []
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spacing = int(sample_rate * 0.001)
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mx = max(song_data)
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@ -77,6 +83,7 @@ def to_ms(song_data, sample_rate, offset):
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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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subprocess.run(["ffmpeg", "-ss", str(offset), "-t", str(length), "-i", song_name, "crop.wav"])
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@ -117,8 +124,43 @@ def filter_n_percent(song_name, offset, length, threshold, reduce, show):
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return song_data
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def filter_n_percent_serial(song_name, offset, n_iter, step, threshold):
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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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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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subprocess.run(["clear"])
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subprocess.run(["rm", "crop.wav"])
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for i in range(n_iter):
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print(i, "/", n_iter)
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song_data = global_data[int(i*step*sample_rate):int((i+1)*step*sample_rate)]
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mx = max(song_data)
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is_locked = [False for i in range(len(song_data))]
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x = int((len(song_data)*threshold)//100)
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#print("X = ", x)
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#print("Retreiving the", int(x), "/", len(song_data), "highest values")
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elements = heapq.nlargest(int(x), enumerate(song_data), key=lambda x: x[1])
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#print("Done")
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for idx in range(len(elements)):
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is_locked[elements[idx][0]] = True
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for r in range(len(song_data)):
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if(is_locked[r] == False):
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global_data[r+int(i*step*sample_rate)] = 0
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return global_data
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def write_to_file_thr(sample_rate, song_data, offset, threshold, filename):
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# write data to output file
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file = open(filename, 'w')
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file.writelines('time,amplitude\n')
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mx = max(song_data)
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@ -132,22 +174,6 @@ def write_to_file_thr(sample_rate, song_data, offset, threshold, filename):
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file.writelines(str(np.round(song_data[i], 0)))
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file.writelines('\n')
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def smooth(data, thr, mergeThr, show):
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mx = max(data)
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for i in range(len(data)-mergeThr):
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if(data[i]/mx > thr):
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for k in range(1, mergeThr):
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data[i+k] = 0
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if(show):
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t = [j/1000 for j in range(len(data))]
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plt.plot(t, data)
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plt.xlabel("Time (not scaled to origin)")
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plt.ylabel("Amplitude")
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plt.grid()
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plt.show()
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return data
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def round_t(id, sample_rate, bpm, div, offset, k0):
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k = k0
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t = offset + k/(bpm*div)
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@ -159,7 +185,8 @@ def round_t(id, sample_rate, bpm, div, offset, k0):
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return t
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return (t - 1/(bpm*div), 0)
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def snap(data, sample_rate, bpm, offset, divisor, show):
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def snap(data, sample_rate, bpm, divisor, show=False):
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# adjust time amplitudes to match the given BPM
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new = [0 for x in range(int(1000*len(data)/sample_rate))] # 1pt per millisecond
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print("old =", len(data))
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print("len =", 1000*len(data)/sample_rate)
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@ -172,6 +199,11 @@ def snap(data, sample_rate, bpm, offset, divisor, show):
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t = k/(bpm*divisor)
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k += 60
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'''
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if(np.abs(i/sample_rate - k/(bpm*divisor)) > np.abs(i/sample_rate - (k-60)/(bpm*divisor))):
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k -= 60
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t = k/(bpm*divisor)'''
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if(i%(len(data)//100) == 0):
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print(percent, "%")
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percent += 1
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@ -190,27 +222,209 @@ def snap(data, sample_rate, bpm, offset, divisor, show):
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plt.show()
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return new
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if(True):
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#data = filter_n_percent("worlds_end_3.wav", 74.582, 30, 0.3, reduce=False, show=False)
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#data = filter_n_percent("no.wav", 1, 15, 0.3)
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#da = find_bpm(44100, data, 100, 200, 1, 0)
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# def find_bpm_2(sample_rate, data, threshold, maxbpm):
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#da = find_bpm_2(44100, data, 0.92, 240, show=False)
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#print("BPM is", da[0], "with std of", da[1])
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def compress(Zxx):
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res = []
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def get_freq(song_name, offset, step, songlen, data, display=False):
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fft_list = []
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times = []
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current_time = offset
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k = 0
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subprocess.run(["ffmpeg", "-ss", str(offset), "-t", str(offset+songlen), "-i", song_name, "crop.wav"])
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sample_rate, global_data = wavfile.read("crop.wav")
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#blit = int(len(global_data) / len(data))
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blit = int(sample_rate*step)
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subprocess.run(["clear"])
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subprocess.run(["rm", "crop.wav"])
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pfreq = scipy.fft.rfftfreq(blit, 1/sample_rate)
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print("len : ", len(global_data))
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print("len : ", len(data))
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frequencies = [0 for s in range(len(data))]
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print(len(pfreq))
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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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mx = max(np.abs(pff))
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for id in range(len(pff)):
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if frequencies[s] == 0 and np.abs(pff[id]) == mx:
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frequencies[s] = pfreq[id]
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elif s != 0:
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frequencies[s] = 0
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if(display):
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plt.plot([t/1000 for t in range(len(data))], frequencies)
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plt.grid()
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plt.xlabel("Time (s)")
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plt.ylabel("Dominant frequency (Hz)")
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plt.title("Dominant frequencies at peaks")
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plt.show()
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return frequencies
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def void_freq(song_name, offset, songlen, increment, minfreq, maxfreq, upperthr, ampthr, ampfreq, ampval, leniency, write, output_file="trimmed.wav"):
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fft_list = []
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times = []
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current_time = offset
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k = 0
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subprocess.run(["ffmpeg", "-ss", str(offset), "-t", str(songlen+offset), "-i", song_name, "crop.wav"])
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sample_rate, global_data = wavfile.read("crop.wav")
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blit = int(sample_rate*increment)
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subprocess.run(["clear"])
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subprocess.run(["rm", "crop.wav"])
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#print("Blit :", blit)
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pfreq = scipy.fft.rfftfreq(blit, 1/sample_rate)
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#print(len(pfreq))
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while(current_time <= songlen):
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pff = scipy.fft.rfft(global_data[k*blit:(k+1)*blit])
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fft_list.append(pff)
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times.append(k*increment)
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k += 1
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current_time = offset + k*increment
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print("FFT :", len(fft_list), "\nFFT[0] :", len(fft_list[0]), "\npfreq :", len(pfreq))
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print("Finding global max...")
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for i in range(len(fft_list)):
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for j in range(len(fft_list[i])):
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fft_list[i][j] *= (1 + ampval/max(1, np.abs(pfreq[j] - ampfreq)))
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print("Trimming...")
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for i in range(len(fft_list)):
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lmax = 0
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for j in range(len(fft_list[i])):
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if(np.abs(fft_list[i][j]) > lmax):
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lmax = np.abs(fft_list[i][j])
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for j in range(len(fft_list[i])):
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if((pfreq[j] >= minfreq and pfreq[j] < maxfreq) or pfreq[j] > upperthr):
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fft_list[i][j] = 0+0j
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if(np.abs(fft_list[i][j]) < lmax/ampthr):
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fft_list[i][j] = 0+0j
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if(write):
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res = []
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print("Converting...")
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for i in range(len(fft_list)):
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ift = scipy.fft.irfft(fft_list[i], n=blit)
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for k in ift:
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res.append(k)
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#print(type(res[0]))
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mx = 0
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for j in range(len(res)):
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if(res[j] > mx):
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mx = res[j]
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for i in range(len(res)):
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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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#plt.plot(np.abs(pfreq[:len(fft_list[0])]), np.abs(fft_list[0]))
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#plt.grid()
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#plt.show()
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print("Done")
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def get_tpts(data, sample_rate, thr):
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res = []
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for i in range(len(data)):
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if(data[i] > thr):
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res.append(i/sample_rate)
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for i in res:
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print(i)
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return res
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def test_sample(timelist):
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for i in range(1,len(timelist)):
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#os.system('play -n synth %s sin %s' % (0.05, 440))
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for k in range(random.randint(1, 10)):
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print("E", end="")
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print("F")
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sleep(timelist[i]-timelist[i-1])
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#Offset = 74.582
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#BPM = 178
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#Length = 48*60/BPM-0.01
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#Offset = 0
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#BPM = 180
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#Length = 48*60/BPM-0.01
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#Offset = 7
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#BPM = 140
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#Length = 32*60/BPM-0.01
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def convert_tuple(datares, freq):
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"""
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Takes datares and converts it to a list of tuples (amplitude, time in ms)
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"""
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return [(i, datares[i], freq[i]) for i in range(len(datares)) if datares[i] > 0]
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def process_song(filename, offset, bpm, div_len_factor=60, n_iter=48, threshold=0.5, divisor=4):
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#zaejzlk
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div_len = div_len_factor/bpm-0.01
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filtered_name = f"{filename}_trimmed.wav"
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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 = 7.27, leniency = 0.005, write=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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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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def main():
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data = process_song("tetris_4.wav", 0, 160)
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print(data)
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print("Program finished with return 0")
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if __name__ == "__main__":
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main()
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data2 = filter_n_percent("worlds_end_3.wav", 74.582, 15, 0.2, reduce=False, show=True)
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data2 = snap(data2, 44100, 178, 74.582, 4, show=True)
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write_to_file_thr(1000, data2, 74.582, 0.02, "timing_points.csv")
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'''
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data2 = filter_n_percent("no.wav", 1, 30, 0.8, reduce=True, show=True)
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write_to_file_thr(1000, smooth(data2, 0.5, 50, show=True), 1, 0.02, "timing_points.csv")
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'''
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#data = to_ms(data, 44100, 1)
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print("Program finished with return 0")
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@ -220,6 +434,21 @@ print("Program finished with return 0")
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'''
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def smooth(data, thr, mergeThr, show):
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mx = max(data)
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for i in range(len(data)-mergeThr):
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if(data[i]/mx > thr):
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for k in range(1, mergeThr):
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data[i+k] = 0
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if(show):
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t = [j/1000 for j in range(len(data))]
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plt.plot(t, data)
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plt.xlabel("Time (not scaled to origin)")
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plt.ylabel("Amplitude")
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plt.grid()
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plt.show()
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return data
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if(False):
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#t, f, Zxx = fct("no.wav", 0, 0.032, 10, 5000, False)
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#t, f, Zxx = fct("worlds_end_3.wav", 150.889, 0.032, 170.889, 3000, False)
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@ -489,3 +718,95 @@ def find_bpm(sample_rate, data, minbpm, maxbpm, step, width):
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return (optimal, optimal_acc)
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'''
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'''
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def void_freq(song_name, offset, songlen, increment, lthr, gthr):
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to_cut = 20000//2500
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global_Zxx = np.array([])
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global_f = np.array([])
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global_t = np.array([])
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current_time = offset
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k = 0
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sample_rate, global_data = wavfile.read(song_name)
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blit = int(sample_rate*increment)
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print("Blit :", blit)
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while(current_time <= songlen):
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#subprocess.run(["ffmpeg", "-ss", str(current_time), "-t", str(increment), "-i", song_name, "crop.wav"])
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#sample_rate, audio_data = wavfile.read('crop.wav')
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audio_data = global_data[int(k*blit):int((k+1)*blit)]
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size = audio_data.size
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#subprocess.run(["clear"])
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#subprocess.run(["rm", "crop.wav"])
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|
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# do stuff here
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#f, t, Zxx = signal.stft(audio_data, sample_rate, nperseg=1000)
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f, t, Zxx = signal.spectrogram(audio_data, fs=sample_rate, nfft=size)
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leng = len(f)
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f, Zxx = f[:leng//to_cut], Zxx[:leng//to_cut]
|
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|
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for i in range(len(Zxx)):
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for j in range(len(Zxx[i])):
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#Zxx[i][j] *= 1127*np.log(1+f[i]/700)
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Zxx[i][j] *= 1000
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t = np.array([current_time + x for x in t])
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if(k == 0):
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global_f = f
|
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global_t = t
|
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global_Zxx = Zxx
|
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else:
|
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global_Zxx = np.concatenate((global_Zxx, Zxx), axis=1)
|
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global_t = np.concatenate((global_t, t))
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#print(len(global_t))
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k += 1
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current_time = offset + k*increment
|
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print("Completion rate : ", np.round(100*(current_time-offset)/(songlen-offset), 4), "%")
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print("Finding global max...")
|
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gmax = 0
|
||||
for i in range(len(global_Zxx)):
|
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for j in range(len(global_Zxx[i])):
|
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if(global_Zxx[i][j] > gmax):
|
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gmax = global_Zxx[i][j]
|
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print("Trimming...")
|
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for j in range(len(global_Zxx[0])):
|
||||
lmax = 0
|
||||
for i in range(len(global_Zxx)):
|
||||
if(global_Zxx[i][j] > lmax):
|
||||
lmax = global_Zxx[i][j]
|
||||
|
||||
for i in range(len(global_Zxx)):
|
||||
val = global_Zxx[i][j]
|
||||
if(val/lmax <= lthr/100):
|
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global_Zxx[i][j] = 0
|
||||
elif(val/gmax <= gthr/100):
|
||||
global_Zxx[i][j] = 0
|
||||
|
||||
if(False):
|
||||
print("Plotting...")
|
||||
plt.pcolormesh(global_t, global_f, np.abs(global_Zxx), shading='gouraud')
|
||||
# print(len(global_Zxx), len(global_Zxx[0]))
|
||||
print("XLEN :", len(global_Zxx), "\nYLEN :", len(global_Zxx[0]))
|
||||
plt.title('STFT Magnitude')
|
||||
plt.ylabel('Frequency [Hz]')
|
||||
plt.xlabel('Time [sec]')
|
||||
plt.show()
|
||||
|
||||
if(True):
|
||||
print("Converting...")
|
||||
audio_signal = librosa.griffinlim(global_Zxx)
|
||||
#scipy.io.wavfile.write('trimmed.wav', sample_rate, np.array(audio_signal, dtype=np.int16))
|
||||
wavfile.write('test.wav', sample_rate, np.array(audio_signal, dtype=np.int16))
|
||||
|
||||
print("Done")
|
||||
'''
|
||||
|
|
|
|||
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Reference in New Issue