mp2i-vms.fr
stats changes
This commit is contained in:
parent
421cddf267
commit
7615d41c01
571
cleaned_sp.py
571
cleaned_sp.py
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@ -20,6 +20,317 @@ def is_data_stereo(raw_global_data:list) -> bool:
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return True
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return True
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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, ampthr=250):
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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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fft_list_untouched = []
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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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fft_list_untouched.append([ee for ee in 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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last_freq = new_freqs[0]
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for i in range(len(new_times)):
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if(np.abs(new_ampls[i]) > ampthr and (i == 0 or not is_note_within(new_freqs[i], last_freq))):
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last_freq = new_freqs[i]
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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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mx = max(new_ampls)
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plt.plot(new_times, new_freqs)
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plt.plot(new_times, [elt*1000/mx for elt in new_ampls])
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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_untouched[i][write_id] = max(maxamp*2, 32767)
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fft_list_untouched[i][write_id-1] = max(maxamp*2, 32767)
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fft_list_untouched[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_untouched)):
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ift = scp.fft.irfft(fft_list_untouched[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 convert_to_wav(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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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], shell=False)
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return output_file
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return song_name
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def retrieve_all_from_song(filename, t0, t1, bpm, dta=0.001, dtf=0.01, threshold=0.06, show=True):
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# dt = sample interval
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# threshold is in percent
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if(t1 <= t0):
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print("ERROR : t1 <= t0\n")
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exit(1)
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# converts format to .wav
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new_fn = convert_to_wav(filename)
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print("Filtering song...")
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#void_freq_clean(new_fn, t0, t1, dtf, 20, 20000, 0.05, "crop1.wav")
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#def void_freq_clean(song_name, offset, songlen, segsize, minfreq, maxfreq, ampthr, output_name):
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print("Now retrieving the frequencies")
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(maxlist, maxamps) = retrieve_dominant_freqs(new_fn, t0, t1, dtf)
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#def retrieve_dominant_freqs(song_name, offset, songlen, segsize):
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print("Now retrieving the amplitudes")
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amps = retrieve_dominant_amps(new_fn, t0, t1, dta, threshold, (4/(bpm/60))/4)
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print("Len of freqs : ", len(maxlist), "|", len(maxamps))
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print("Len of amps : ", len(maxlist), "|", len(amps))
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maxa = amps[0]
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for jj in amps:
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if(jj > maxa):
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maxa = jj
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for i in range(len(amps)):
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amps[i] = (amps[i] * 2000) / maxa
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if(show):
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timesF = [t0 + dtf*k for k in range(len(maxlist))]
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timesA = [t0 + dta*k for k in range(len(amps))]
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plt.plot(timesA, amps)
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plt.plot(timesF, maxlist)
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plt.show()
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# free()
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'''
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# c-type
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SONG_LEN = 8
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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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wavved_song = convert_to_wav("songs/ctype.mp3")
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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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# 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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wavved_song = convert_to_wav("songs/furioso melodia.mp3")
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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("songs/rushe.mp3")
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'''
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'''
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# Tsubaki
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SONG_LEN = 10
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OFFSET = 35.659
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BPM = 199
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SEGSIZE = 1/(BPM/60)
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wavved_song = convert_to_wav("songs/TSUBAKI.mp3")
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'''
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'''
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# death
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SONG_LEN = 8
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OFFSET = 21.750
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BPM = 180
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SEGSIZE = 1/(BPM/60)
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wavved_song = convert_to_wav("songs/Night of Knights.mp3")
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'''
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#wavved_song = convert_to_wav("tetris_2.wav")
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NOTE_DIST = (2**(1/4))
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keep_highest(wavved_song, OFFSET, SONG_LEN, SEGSIZE/4, 1, "Zblit.wav", minfreq=300, maxfreq=3000, ampthr=500)
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print("yipee")
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'''
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def retrieve_dominant_freqs(song_name, offset, songlen, segsize):
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# returns a list with peak frequencies alongside the sample rate
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# /!\ song_name is specified to be a list, NOT a list of couples (aka song is mono)
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@ -324,166 +635,6 @@ 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)
|
||||
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_name, sample_rate, res)
|
||||
|
||||
def write_result(song_name, offset, songlen, segsize, timing_pts, output_name):
|
||||
# removes unnecessary frequencies/amps from a song
|
||||
# ampthr is in [0, 1]
|
||||
|
|
@ -751,103 +902,6 @@ def parse_after_filter(song_name, offset, songlen, segsize, dt0):
|
|||
print(timing_points)
|
||||
return timing_points
|
||||
|
||||
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 retrieve_all_from_song(filename, t0, t1, bpm, dta=0.001, dtf=0.01, threshold=0.06, show=True):
|
||||
# dt = sample interval
|
||||
# threshold is in percent
|
||||
|
||||
if(t1 <= t0):
|
||||
print("ERROR : t1 <= t0\n")
|
||||
exit(1)
|
||||
|
||||
# converts format to .wav
|
||||
new_fn = convert_to_wav(filename)
|
||||
|
||||
print("Filtering song...")
|
||||
#void_freq_clean(new_fn, t0, t1, dtf, 20, 20000, 0.05, "crop1.wav")
|
||||
#def void_freq_clean(song_name, offset, songlen, segsize, minfreq, maxfreq, ampthr, output_name):
|
||||
|
||||
print("Now retrieving the frequencies")
|
||||
(maxlist, maxamps) = retrieve_dominant_freqs(new_fn, t0, t1, dtf)
|
||||
#def retrieve_dominant_freqs(song_name, offset, songlen, segsize):
|
||||
|
||||
print("Now retrieving the amplitudes")
|
||||
amps = retrieve_dominant_amps(new_fn, t0, t1, dta, threshold, (4/(bpm/60))/4)
|
||||
|
||||
print("Len of freqs : ", len(maxlist), "|", len(maxamps))
|
||||
print("Len of amps : ", len(maxlist), "|", len(amps))
|
||||
|
||||
maxa = amps[0]
|
||||
for jj in amps:
|
||||
if(jj > maxa):
|
||||
maxa = jj
|
||||
|
||||
for i in range(len(amps)):
|
||||
amps[i] = (amps[i] * 2000) / maxa
|
||||
|
||||
if(show):
|
||||
timesF = [t0 + dtf*k for k in range(len(maxlist))]
|
||||
timesA = [t0 + dta*k for k in range(len(amps))]
|
||||
|
||||
plt.plot(timesA, amps)
|
||||
plt.plot(timesF, maxlist)
|
||||
plt.show()
|
||||
|
||||
# free()
|
||||
'''
|
||||
# c-type
|
||||
SONG_LEN = 5
|
||||
OFFSET = 0.042
|
||||
BPM = 149.3
|
||||
SEGSIZE = 1/(BPM/60)
|
||||
'''
|
||||
'''
|
||||
# tetris_2
|
||||
SONG_LEN = 8
|
||||
OFFSET = 0
|
||||
BPM = 157
|
||||
SEGSIZE = 1/(BPM/60)
|
||||
'''
|
||||
'''
|
||||
# test
|
||||
SONG_LEN = 1
|
||||
OFFSET = 0
|
||||
BPM = 240
|
||||
SEGSIZE = 1/(BPM/60)
|
||||
'''
|
||||
'''
|
||||
# gmtn
|
||||
SONG_LEN = 3
|
||||
OFFSET = 1.652
|
||||
BPM = 155
|
||||
SEGSIZE = 1/(BPM/60)
|
||||
'''
|
||||
# E
|
||||
SONG_LEN = 10
|
||||
OFFSET = 2.641
|
||||
BPM = 155
|
||||
SEGSIZE = 1/(BPM/60)
|
||||
|
||||
#wavved_song = convert_to_wav("tetris_2.wav")
|
||||
wavved_song = convert_to_wav("songs/rushe.mp3")
|
||||
|
||||
keep_highest(wavved_song, OFFSET, SONG_LEN, SEGSIZE/4, 1, "Zblit.wav", minfreq=300, maxfreq=3000)
|
||||
|
||||
|
||||
'''
|
||||
# remove high/low frequencies (often noise)
|
||||
#void_freq_clean(wavved_song, OFFSET, SONG_LEN, SEGSIZE/8, 100, 3000, 0.05, "Zvoided_song.wav")
|
||||
|
||||
|
|
@ -863,9 +917,6 @@ write_result(wavved_song, OFFSET, SONG_LEN-0.1, SEGSIZE/8, tp, "Zoutput_song.wav
|
|||
|
||||
#retrieve_all_from_song("Zcleaned_song.wav", 0, 5, 149.3, dtf=1/(149.3/60)/8)
|
||||
'''
|
||||
print("yipee")
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
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Reference in New Issue