general refactoring of folder + rework of most functions
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a59c0c4e08
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Zoutput_song.wav
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Zoutput_song.wav
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Zvoided_song.wav
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Zvoided_song.wav
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215
cleaned_sp.py
215
cleaned_sp.py
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@ -324,6 +324,166 @@ def localize_frequencies(song_name, offset, songlen, segsize, output_name):
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res = np.array(res)
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wavfile.write(output_name, sample_rate, res)
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NOTE_DIST = (2**(1/12))
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def is_note_within(fr1, fr2):
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if(fr1 > fr2):
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return (fr1/fr2 <= NOTE_DIST)
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else:
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return (fr2/fr1 <= NOTE_DIST)
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def keep_highest(song_name, offset, songlen, segsize, count, output_name, minfreq=110, maxfreq=5000):
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# extracting data from cropped song
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sample_rate, raw_song_data = wavfile.read(song_name)
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blit = int(sample_rate*segsize) # Te
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song_data = [0 for i in range(len(raw_song_data))]
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id_start = int(offset*sample_rate)
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id_end = min(len(raw_song_data), int((offset+songlen)*sample_rate))
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a = 0
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if(is_data_stereo(raw_song_data)):
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print("Converting to mono...")
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for x in range(id_start, id_end):
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song_data[x] = raw_song_data[x][0]/2 + raw_song_data[x][1]/2
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if(x % (int(len(raw_song_data)/100)) == 0):
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print(a, "/ 100")
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a += 1
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else:
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song_data = raw_song_data
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print("\nSampleRate : ", sample_rate)
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print("SegSize : ", blit)
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# calculate the frequencies associated to the FFTs
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pfreq = scp.fft.rfftfreq(blit, 1/sample_rate)
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# left boundary of segment to crop
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current_time = offset
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# list of FFTs
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fft_list = []
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# number of samples
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k = 0
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print("Retrieving freqs from", offset, "to", songlen+offset, "...")
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while(current_time < songlen+offset-segsize):
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# index corresponding to left boundary
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left_id = int(current_time*sample_rate)
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# index corresponding to right boundary
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right_id = int((current_time+segsize)*sample_rate)
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# calculate the fft, append it to fft_list
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pff = scp.fft.rfft(song_data[int(current_time*sample_rate):int(sample_rate*(current_time+segsize))])
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fft_list.append(pff)
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# just to avoid what causes 0.1 + 0.1 == 0.2 to be False
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k += 1
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current_time = offset + k*segsize
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#print(current_time)
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print("\n\nSegSize :", segsize, "\nFFT :", len(fft_list), "\nFFT[0] :", len(fft_list[0]), "\npfreq :", len(pfreq), "\n\n")
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# -------------------------------------------- Clean song -------------------------------------------- #
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pfreq_minid = 0
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pfreq_maxid = len(pfreq) -1
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while(pfreq[pfreq_minid] < minfreq):
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for t in range(len(fft_list)):
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fft_list[t][pfreq_minid] = 0+0j
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pfreq_minid += 1
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while(pfreq[pfreq_maxid] > maxfreq):
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for t in range(len(fft_list)):
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fft_list[t][pfreq_maxid] = 0+0j
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pfreq_maxid -= 1
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new_times = []
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new_freqs = []
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new_ampls = []
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new_kept = []
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# i = time, j = freq
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for i in range(len(fft_list)):
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#returns a list of couples [id, value]
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elements = heapq.nlargest(count, enumerate(fft_list[i]), key=lambda x: x[1])
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for idx in range(len(elements)):
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if(elements[idx][0] < len(pfreq)):
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new_times.append(offset + i*segsize)
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new_freqs.append(pfreq[elements[idx][0]])
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new_ampls.append(fft_list[i][elements[idx][0]])
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'''for i in range(len(new_freqs)):
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while(new_freqs[i]>1000):
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new_freqs[i] = new_freqs[i]/2'''
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# -------------------------------------------- Localize -------------------------------------------- #
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timing_points = []
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for i in range(len(new_times)):
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if(i == 0 or not is_note_within(new_freqs[i], new_freqs[i-1])):
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timing_points.append(new_times[i])
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new_kept.append(new_freqs[i])
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else:
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new_kept.append(0)
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plt.plot(new_times, new_freqs)
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plt.plot(new_times, new_kept, "ro")
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plt.grid()
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plt.show()
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# -------------------------------------------- Write -------------------------------------------- #
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i0 = 0
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timing_points.append(999999)
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write_freq = 880
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write_cur = 0
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write_id = -1
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while(write_cur <= write_freq): # shouldnt seg fault
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write_id += 1
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write_cur = pfreq[write_id]
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# remove
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# i = time, j = freq
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for i in range(len(fft_list)):
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# retrieve dominant freq
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if(segsize*i >= timing_points[i0]-offset):
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i0 += 1
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maxfreq = 0
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maxfreqid = 0
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maxamp = 0
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for j in range(len(fft_list[0])):
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if(np.abs(fft_list[i][j]) > maxamp):
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maxamp = np.abs(fft_list[i][j])
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maxfreq = pfreq[j]
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maxfreqid = j
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fft_list[i][write_id] = max(maxamp*2, 32767)
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fft_list[i][write_id-1] = max(maxamp*2, 32767)
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fft_list[i][write_id+1] = max(maxamp*2, 32767)
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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 = scp.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_name, sample_rate, res)
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def write_result(song_name, offset, songlen, segsize, timing_pts, output_name):
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# removes unnecessary frequencies/amps from a song
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# ampthr is in [0, 1]
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@ -407,7 +567,9 @@ def write_result(song_name, offset, songlen, segsize, timing_pts, output_name):
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maxfreq = pfreq[j]
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maxfreqid = j
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fft_list[i][write_id] = max(maxamp*2, 10000)
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fft_list[i][write_id] = max(maxamp*2, 32767)
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fft_list[i][write_id-1] = max(maxamp*2, 32767)
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fft_list[i][write_id+1] = max(maxamp*2, 32767)
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# writing new .wav
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@ -645,27 +807,53 @@ def retrieve_all_from_song(filename, t0, t1, bpm, dta=0.001, dtf=0.01, threshold
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plt.show()
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# free()
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'''
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void_freq_clean(convert_to_wav("ctype.mp3"), 0.042, 5, 1/(149.3/60)/8, 100, 3000, 0.05, "ctype_void.mp3")
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localize_frequencies(convert_to_wav("ctype_void.mp3"), 0, 5, 1/(149.3/60)/12, "ctype_filtered.mp3")
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retrieve_all_from_song("ctype_filtered.mp3", 0, 5, 149.3, dta=1/(149.3/60)/128, dtf=1/(149.3/60)/8)
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'''
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#OFFSET = 0.042
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#BPM = 149.3
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# c-type
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SONG_LEN = 5
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OFFSET = 117.790
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BPM = 150
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OFFSET = 0.042
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BPM = 149.3
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SEGSIZE = 1/(BPM/60)
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'''
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'''
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# tetris_2
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SONG_LEN = 8
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OFFSET = 0
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BPM = 157
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SEGSIZE = 1/(BPM/60)
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'''
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'''
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# test
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SONG_LEN = 1
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OFFSET = 0
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BPM = 240
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SEGSIZE = 1/(BPM/60)
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'''
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'''
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# gmtn
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SONG_LEN = 3
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OFFSET = 1.652
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BPM = 155
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SEGSIZE = 1/(BPM/60)
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'''
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# E
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SONG_LEN = 10
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OFFSET = 2.641
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BPM = 155
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SEGSIZE = 1/(BPM/60)
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wavved_song = convert_to_wav("Galaxy Collapse.mp3")
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#wavved_song = convert_to_wav("tetris_2.wav")
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wavved_song = convert_to_wav("songs/rushe.mp3")
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keep_highest(wavved_song, OFFSET, SONG_LEN, SEGSIZE/4, 1, "Zblit.wav", minfreq=300, maxfreq=3000)
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'''
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# remove high/low frequencies (often noise)
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#void_freq_clean(wavved_song, OFFSET, SONG_LEN, SEGSIZE/8, 100, 3000, 0.05, "Zvoided_song.wav")
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# crops any part with let ring
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localize_frequencies(convert_to_wav("Zvoided_song.wav"), 0, SONG_LEN-0.1, SEGSIZE/8, "Zcleaned_song.wav")
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localize_frequencies("Zblit.wav", 0, SONG_LEN-0.1, SEGSIZE/8, "Zcleaned_song.wav")
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#localize_frequencies(wavved_song, OFFSET, SONG_LEN, SEGSIZE/8, "Zcleaned_song.wav")
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# find timings
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tp = parse_after_filter("Zcleaned_song.wav", 0, SONG_LEN-0.1, SEGSIZE/8, OFFSET)
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@ -674,6 +862,7 @@ tp = parse_after_filter("Zcleaned_song.wav", 0, SONG_LEN-0.1, SEGSIZE/8, OFFSET)
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write_result(wavved_song, OFFSET, SONG_LEN-0.1, SEGSIZE/8, tp, "Zoutput_song.wav")
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#retrieve_all_from_song("Zcleaned_song.wav", 0, 5, 149.3, dtf=1/(149.3/60)/8)
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'''
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print("yipee")
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343
sound_process.py
343
sound_process.py
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@ -1,343 +0,0 @@
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from math import *
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import numpy as np
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from scipy.io import wavfile
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from scipy import signal
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import matplotlib.pyplot as plt
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import subprocess
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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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from datetime import timedelta
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import debug
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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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"""
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song_name : string
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offset : int
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n_iter : int (number of turns)
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step : int (length of each small segment)
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threshold : int (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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"""
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subprocess.run(["ffmpeg", "-ss", str(offset), "-t", str(offset+step*n_iter), "-i", song_name, "crop.wav"], shell=False)
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sample_rate, global_data = wavfile.read('crop.wav')
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subprocess.run(["clear"], shell=False)
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subprocess.run(["rm", "crop.wav"], shell=False)
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for i in range(n_iter):
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print(i, "/", n_iter)
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#print(i * step)
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song_data = global_data[int(i*step*sample_rate):int((i+1)*step*sample_rate)]
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if(len(song_data) != 0):
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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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print("writing to output...")
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for i in range(len(song_data)):
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if(i%(len(song_data)//50) == 0):
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print(i, "/", len(song_data))
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if(song_data[i]/mx > threshold):
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file.writelines(str(np.round(offset + i/sample_rate, 3)))
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file.writelines(',')
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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 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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while(t < id/sample_rate):
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t = offset + k/(bpm*div)
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k += 1
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if(np.abs(t - id/sample_rate) < np.abs((t - 1/(bpm*div)) - id/sample_rate)):
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return t
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return (t - 1/(bpm*div), 0)
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def compress(Zxx):
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res = []
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def get_freq(song_name, times, width=1000, display=False):
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"""
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for a given list of times (in seconds), returns the corresponding peak frequencies
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"""
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subprocess.run(["ffmpeg", "-ss", str(0), "-t", str(max(np.array(times))), "-i", song_name, "crop.wav"], shell=False)
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sample_rate, global_data = wavfile.read(song_name)
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#blit = int(sample_rate*step)
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subprocess.run(["clear"], shell=False)
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subprocess.run(["rm", "crop.wav"], shell=False)
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pfreq = scipy.fft.rfftfreq(2*width, 1/sample_rate)
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frequencies = [0 for s in range(len(times))]
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print(len(pfreq))
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for s in range(len(times)):
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left = max(0, int(times[s]*44100)-width)
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right = min(len(global_data), int(times[s]*44100)+width)
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pff = scipy.fft.rfft(global_data[left:right])
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#print(len(pff), len(pfreq))
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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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if(display):
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plt.plot(times, 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 is_data_stereo(raw_global_data:list) -> bool:
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"""
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raw_global_data : list
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"""
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try:
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assert(raw_global_data[0][0])
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except IndexError:
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return False
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except AssertionError:
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return True
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return True
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def void_freq(song_name, offset, songlen, increment, minfreq, maxfreq, upperthr, ampthr, ampfreq, ampval, leniency, write, linear, output_file="trimmed.wav"):
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"""
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song_name : string
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offset : int
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songlen : int (length of the part that will be filtered, starting from offset)
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increment : float (technical parameter)
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minfreq and maxfreq : every frequency in [minfreq, maxfreq] will be voided
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upperthr : every frequency above upperthr will be voided
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ampthr : every frequency with amplitude under MAX/ampthr (aka amplitudes under (100/ampthr)% of the max will be voided
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ampfreq, leniency (if linear is false), linear : technical parameters
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ampval : int
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- if linear is false, then this willbe the maximum amplification possible
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- if linear is true, this is the multiplier (Amp <- Amp * (ampval * frequency + leniency))
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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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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"], shell=False)
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sample_rate, raw_global_data = wavfile.read("crop.wav")
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blit = int(sample_rate*increment)
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global_data = [0 for i in range(len(raw_global_data))]
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#subprocess.run(["clear"])
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subprocess.run(["rm", "crop.wav"], shell=False)
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a = 0
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if(is_data_stereo(raw_global_data)):
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print("Converting to mono...")
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for x in range(len(raw_global_data)):
|
||||
global_data[x] = raw_global_data[x][0]/2 + raw_global_data[x][1]/2
|
||||
|
||||
if(x % (int(len(raw_global_data)/100)) == 0):
|
||||
print(a, "/ 100")
|
||||
a += 1
|
||||
|
||||
else:
|
||||
global_data = raw_global_data
|
||||
|
||||
#print("Blit :", blit)
|
||||
|
||||
pfreq = scipy.fft.rfftfreq(blit, 1/sample_rate)
|
||||
|
||||
#print(len(pfreq))
|
||||
|
||||
while(current_time <= songlen):
|
||||
pff = scipy.fft.rfft(global_data[k*blit:(k+1)*blit])
|
||||
fft_list.append(pff)
|
||||
times.append(k*increment)
|
||||
|
||||
k += 1
|
||||
current_time = offset + k*increment
|
||||
|
||||
print("FFT :", len(fft_list), "\nFFT[0] :", len(fft_list[0]), "\npfreq :", len(pfreq))
|
||||
|
||||
|
||||
print("Finding global max...")
|
||||
|
||||
if(linear == False):
|
||||
for i in range(len(fft_list)):
|
||||
for j in range(len(fft_list[i])):
|
||||
fft_list[i][j] *= (1 + ampval/max(1, np.abs(pfreq[j] - ampfreq)))
|
||||
else:
|
||||
for i in range(len(fft_list)):
|
||||
for j in range(len(fft_list[i])):
|
||||
fft_list[i][j] *= (ampval*pfreq[j] + leniency)
|
||||
|
||||
print("Trimming...")
|
||||
|
||||
for i in range(len(fft_list)):
|
||||
lmax = 0
|
||||
for j in range(len(fft_list[i])):
|
||||
if(np.abs(fft_list[i][j]) > lmax):
|
||||
lmax = np.abs(fft_list[i][j])
|
||||
|
||||
for j in range(len(fft_list[i])):
|
||||
if((pfreq[j] >= minfreq and pfreq[j] < maxfreq) or pfreq[j] > upperthr):
|
||||
fft_list[i][j] = 0+0j
|
||||
|
||||
if(np.abs(fft_list[i][j]) < lmax/ampthr):
|
||||
fft_list[i][j] = 0+0j
|
||||
|
||||
|
||||
if(write):
|
||||
res = []
|
||||
print("Converting...")
|
||||
for i in range(len(fft_list)):
|
||||
ift = scipy.fft.irfft(fft_list[i], n=blit)
|
||||
for k in ift:
|
||||
res.append(k)
|
||||
#print(type(res[0]))
|
||||
mx = 0
|
||||
for j in range(len(res)):
|
||||
if(res[j] > mx):
|
||||
mx = res[j]
|
||||
|
||||
for i in range(len(res)):
|
||||
res[i] = np.int16(32767*res[i]/mx)
|
||||
|
||||
res = np.array(res)
|
||||
wavfile.write(output_file, 44100, res)
|
||||
|
||||
#plt.plot(np.abs(pfreq[:len(fft_list[0])]), np.abs(fft_list[0]))
|
||||
#plt.grid()
|
||||
#plt.show()
|
||||
|
||||
print("Done")
|
||||
|
||||
def convert_tuple(data, times):
|
||||
"""
|
||||
Takes data and converts it to a list of tuples (amplitude, datetimes)
|
||||
"""
|
||||
return [(times[i], data[i]) for i in range(len(data))]
|
||||
|
||||
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 convert_to_wav(song_name:str, output_file="audio.wav") -> str:
|
||||
"""
|
||||
Converts the song to .wav, only if it's not already in wave format.
|
||||
Currently relies on file extension.
|
||||
Returns: the song_name that should be used afterwards.
|
||||
"""
|
||||
extension = Path(song_name).suffix
|
||||
match extension:
|
||||
case ".mp3" | ".ogg":
|
||||
print("Converting to .wav...")
|
||||
subprocess.run(["ffmpeg", "-y", "-i", song_name, output_file], shell=False)
|
||||
return output_file
|
||||
return song_name
|
||||
|
||||
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)
|
||||
bpm : int [+]
|
||||
div_len_factor : float [+] (the length multiplier of each segment, default is 1)
|
||||
n_iter : int [+*] (the number of iterations, default is -1 (maps the whole music))
|
||||
threshold : int [0, 100] (used by the filter function to only keep the largest threshold% of timing points, default is 0.5)
|
||||
divisor : int [+] (beat divisor used to snap the notes, default is 4)
|
||||
"""
|
||||
|
||||
filename = convert_to_wav(filename)
|
||||
|
||||
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):
|
||||
n_iter = int((song_len-offset/1000)/div_len)-1
|
||||
|
||||
filtered_name = f"{filename}_trimmed.wav"
|
||||
|
||||
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)
|
||||
|
||||
datares = filter_n_percent_serial(filtered_name, offset, n_iter, div_len, threshold)
|
||||
|
||||
#snapped_data = amplitude
|
||||
#times in ms
|
||||
(snapped_data, times) = debug.snap3(datares, mintime=50, initial_plot=True, after_plot=True)
|
||||
|
||||
#frequencies=get_freq(filtered_name, offset, div_len, div_len*n_iter, snapped_data, True)
|
||||
frequencies = get_freq(filtered_name, times, display=True)
|
||||
|
||||
Path(f"{filename}_trimmed.wav").unlink()
|
||||
return snapped_data, times, frequencies
|
||||
|
||||
'''
|
||||
datares = debug.snap2(datares, 44100, bpm, first_offset=offset, div=divisor, show=True, adjust=True)
|
||||
frequencies = get_freq(filtered_name, offset, div_len, div_len*n_iter, datares, True)
|
||||
Path(f"{filename}_trimmed.wav").unlink()
|
||||
return convert_tuple(datares, frequencies)
|
||||
'''
|
||||
|
||||
def main():
|
||||
aa, bb, cc = process_song("tetris_4.wav", 160, n_iter_2=48)
|
||||
#print(data)
|
||||
print("Program finished with return 0")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
Loading…
Reference in New Issue