mp2i-vms.fr
813 lines
23 KiB
Python
Executable File
813 lines
23 KiB
Python
Executable File
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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print("Starting...\n")
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def find_bpm_2(sample_rate, data, threshold, maxbpm, show):
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mx = np.max(data)
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min_spacing = (60*sample_rate)/maxbpm
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k = 0
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while(k < len(data) and data[k]/mx < threshold):
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k += 1
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k += 1
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spacing = []
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current = 1
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progress = 0
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while(k < len(data)):
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if(k%(len(data)/100) == 0):
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print(progress, "%")
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progress += 1
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if(data[k]/mx >= threshold and current > min_spacing):
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spacing.append(current)
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current = 0
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else:
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current += 1
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k += 1
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for x in range(len(spacing)):
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spacing[x] = 60/(spacing[x]/sample_rate)
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digits = [i for i in range(len(spacing))]
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if(show):
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plt.plot(digits, spacing)
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plt.xlabel("N")
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plt.ylabel("BPM")
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plt.grid()
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plt.show()
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beat = np.mean(spacing)
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error = np.std(spacing)
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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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i = 0
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while(i < len(song_data)):
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avg = 0
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for k in range(spacing):
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if(i+spacing < len(song_data)):
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avg += song_data[i+spacing]
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avg = avg / spacing
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new_data.append(avg)
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i += spacing
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if(False): # pls dont kill me thx
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t = [offset + j/1000 for j in range(len(new_data))]
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plt.plot(t, new_data)
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plt.xlabel("Time")
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plt.ylabel("Amplitude")
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plt.grid()
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plt.show()
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return (new_data, len(new_data))
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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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sample_rate, song_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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if(reduce):
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(song_data,e) = to_ms(song_data, 44100, 1)
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sample_rate = 1000
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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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song_data[r] = 0
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if(show):
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#print("EEEEE")
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t = [offset + j/sample_rate for j in range(len(song_data))]
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plt.plot(t, song_data)
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plt.xlabel("Time")
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plt.ylabel("Amplitude")
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plt.grid()
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plt.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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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 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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k = 0
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t = 0
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percent = 0
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for i in range(len(data)):
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while(t < i/sample_rate):
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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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if(int(t*1000) < len(new)):
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new[int(t*1000)] = max(data[i], new[int(t*1000)])
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else:
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new[len(new)-1] = max(data[i], new[len(new)-1])
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if(show):
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t = [j/1000 for j in range(len(new))]
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plt.plot(t, new)
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plt.xlabel("Time (e)")
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plt.ylabel("Amplitude")
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plt.grid()
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plt.show()
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return new
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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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''' -------------------------------------------------------------------- '''
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''' -----------------------| Feuilles mortes |-------------------------- '''
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''' -------------------------------------------------------------------- '''
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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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#t, f, Zxx = fct("deltamax.wav", 9.992, 0.032, 114.318, 3000, False)
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#t, f, Zxx = fct("deltamax.wav", 9.992, 0.032, 20, 3000, False)
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#t, f, Zxx = fct("da^9.wav", 8.463, 0.032, 20, 5000, False)
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t, f, Zxx = fct("13. Cosmic Mind.wav", 0, 0.032, 20, 5000, False)
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#t, f, Zxx = fct("Furioso Melodia 44100.wav", 4, 0.032, 8, 3000, False)
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#t, f, Zxx = fct("changing.wav", 0, 0.05, 3.9, 5000, False)
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#fct("worlds_end_3.wav", 75, (60/178)/4, 75+2, 2500)
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plot_max(t, f, Zxx, True)
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if(False):
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#(t, data) = peaks("worlds_end_3.wav", 0, 300, False, 0.92)
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(t, data) = peaks("worlds_end_3.wav", 74.582, 6, False, 0.9)
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#(t, data) = peaks("da^9.wav", 8.463, 301.924 - 8.463, False, 0.95)
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#(t, data) = peaks("deltamax.wav", 8.463, 30101.924 - 8.463, False, 0.92)
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da = find_bpm(t, 44100, data, 100, 200, 1, 10)
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print("BPM data is", da)'''
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#data = [-1 for i in range(int(x))]
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#ids = [-1 for i in range(int(x))]
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'''
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data = []
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ids = []
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for k in range(int(x)):
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data.append(int(7*mx/10))
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ids.append(-1)
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# structure there is [[index, value]...]
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i = 0
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calc = 0
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while(i < len(song_data)):
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if(i%10 == 0):
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print(i, "/", len(song_data))
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if(data[int(x)-1] < song_data[i]):
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calc += 1
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#print("\n \n \n \n \n")
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data[int(x)-1] = song_data[i]
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ids[int(x)-1] = i
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k = int(x)-1
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#while(k < int(x) & data[0] > data[k]):
|
|
while(k > 0 and data[k-1] <= data[k]):
|
|
data[k], data[k-1] = data[k-1], data[k]
|
|
ids[k], ids[k-1] = ids[k-1], ids[k]
|
|
k -= 1
|
|
|
|
#print(data[int(x)-1], calc, "/", x)
|
|
|
|
i += skip
|
|
i += 1
|
|
|
|
|
|
for s in range(int(x)-1):
|
|
if(data[s] < data[s+1]):
|
|
print("Nope", s)
|
|
assert(0)
|
|
'''
|
|
|
|
|
|
'''
|
|
def fct(song_name, offset, increment, songlen, maxfreq, display):
|
|
to_cut = 20000//maxfreq
|
|
global_Zxx = np.array([])
|
|
global_f = np.array([])
|
|
global_t = np.array([])
|
|
current_time = offset
|
|
k = 0
|
|
while(current_time <= songlen):
|
|
subprocess.run(["ffmpeg", "-ss", str(current_time), "-t", str(increment), "-i", song_name, "crop.wav"])
|
|
|
|
sample_rate, audio_data = wavfile.read('crop.wav')
|
|
size = audio_data.size
|
|
|
|
#subprocess.run(["clear"])
|
|
subprocess.run(["rm", "crop.wav"])
|
|
|
|
# do stuff here
|
|
#f, t, Zxx = signal.stft(audio_data, sample_rate, nperseg=1000)
|
|
f, t, Zxx = signal.spectrogram(audio_data, fs=sample_rate, nfft=size)
|
|
leng = len(f)
|
|
|
|
f, Zxx = f[:leng//to_cut], Zxx[:leng//to_cut]
|
|
|
|
#print(len(Zxx))
|
|
#print(len(Zxx[0]))
|
|
|
|
|
|
for i in range(len(Zxx)):
|
|
for j in range(len(Zxx[i])):
|
|
Zxx[i][j] *= 1127*np.log(1+f[i]/700)
|
|
|
|
|
|
t = np.array([current_time + x for x in t])
|
|
|
|
if(k == 0):
|
|
global_f = f
|
|
global_t = t
|
|
global_Zxx = Zxx
|
|
else:
|
|
global_Zxx = np.concatenate((global_Zxx, Zxx), axis=1)
|
|
global_t = np.concatenate((global_t, t))
|
|
|
|
#print(len(global_t))
|
|
|
|
k += 1
|
|
current_time = offset + k*increment
|
|
|
|
print("Completion rate : ", np.round(100*(current_time-offset)/(songlen-offset), 4), "%")
|
|
|
|
if(display):
|
|
plt.pcolormesh(global_t, global_f, np.abs(global_Zxx), shading='gouraud')
|
|
# print(len(global_Zxx), len(global_Zxx[0]))
|
|
# 88 192 = 2500
|
|
# 70 192 = 2000
|
|
plt.title('STFT Magnitude')
|
|
plt.ylabel('Frequency [Hz]')
|
|
plt.xlabel('Time [sec]')
|
|
plt.show()
|
|
|
|
return global_t, global_f, np.abs(global_Zxx)
|
|
|
|
def write_to_file(t, flist, maxlist, filename):
|
|
file = open(filename, 'w')
|
|
file.writelines('time,frequency,maxvalue\n')
|
|
for i in range(len(t)):
|
|
file.writelines(str(np.round(t[i], 3)))
|
|
file.writelines(',')
|
|
file.writelines(str(np.round(flist[i], 1)))
|
|
file.writelines(',')
|
|
file.writelines(str(np.round(maxlist[i], 0)))
|
|
file.writelines('\n')
|
|
#close(file)
|
|
|
|
def plot_max(time, freq, Zxx, save):
|
|
fres = [0 for x in range(len(time))]
|
|
maxres = [0 for x in range(len(time))]
|
|
for t in range(len(time)):
|
|
#subprocess.run(["clear"])
|
|
print(t, "/", len(time))
|
|
for f in range(len(Zxx)):
|
|
if(maxres[t] < Zxx[f][t]):
|
|
maxres[t] = Zxx[f][t]
|
|
fres[t] = freq[f]
|
|
|
|
if(save):
|
|
write_to_file(time, fres, maxres, 'output.csv')
|
|
|
|
''''''
|
|
plt.plot(time, fres, 'r')
|
|
plt.grid()
|
|
plt.xlabel("Time")
|
|
plt.ylabel("Maximum frequencies")
|
|
|
|
plt.plot(time, maxres, 'g')
|
|
plt.grid()
|
|
plt.xlabel("Time")
|
|
plt.ylabel("Maximun values")
|
|
|
|
plt.show()''''''
|
|
|
|
fig, (ax1, ax2) = plt.subplots(2)
|
|
fig.suptitle('Top : time and frequencies\nBottom : time and max values')
|
|
ax1.plot(time, fres)
|
|
ax2.plot(time, maxres)
|
|
|
|
plt.show()
|
|
|
|
def extract_peaks(song_data, sample_rate, offset, display, threshold):
|
|
mx = max(song_data)
|
|
for i in range(len(song_data)):
|
|
#subprocess.run(["clear"])
|
|
print(i, "/", len(song_data))
|
|
if(song_data[i]/mx < threshold):
|
|
song_data[i] = 0
|
|
t = [offset + i/sample_rate for i in range(len(song_data))]
|
|
|
|
if(display):
|
|
plt.plot(t, song_data, 'b+')
|
|
plt.grid()
|
|
plt.xlabel("t")
|
|
plt.ylabel("amp")
|
|
plt.show()
|
|
|
|
return (t, song_data)
|
|
|
|
def get_local_max(song_data, center, width):
|
|
mx = 0
|
|
for o in range(-width, width+1):
|
|
togo = min(len(song_data)-1, center+o)
|
|
togo = max(0, togo)
|
|
if(mx < song_data[togo]):
|
|
mx = song_data[togo]
|
|
return mx
|
|
|
|
def extract_peaks_v2(song_data, sample_rate, offset, display, threshold, seglen):
|
|
mx = 0
|
|
for i in range(len(song_data)):
|
|
if (i%seglen == 0):
|
|
print("----")
|
|
mx = get_local_max(song_data, i+seglen//2, seglen//2)
|
|
#subprocess.run(["clear"])
|
|
print(i, "/", len(song_data))
|
|
if(song_data[i]/mx < threshold):
|
|
song_data[i] = 0
|
|
|
|
t = [offset + i/sample_rate for i in range(len(song_data))]
|
|
|
|
if(display):
|
|
plt.plot(t, song_data, 'b+')
|
|
plt.grid()
|
|
plt.xlabel("t")
|
|
plt.ylabel("amp")
|
|
plt.show()
|
|
|
|
return (t, song_data)
|
|
|
|
def peaks(song_name, offset, length, display, thr):
|
|
subprocess.run(["ffmpeg", "-ss", str(offset), "-t", str(length), "-i", song_name, "crop.wav"])
|
|
|
|
sample_rate, audio_data = wavfile.read('crop.wav')
|
|
|
|
subprocess.run(["clear"])
|
|
subprocess.run(["rm", "crop.wav"])
|
|
|
|
#return extract_peaks(audio_data, sample_rate, offset, display, thr)
|
|
return extract_peaks_v2(audio_data, sample_rate, offset, display, thr, 44100*2)
|
|
|
|
def find_bpm(sample_rate, data, minbpm, maxbpm, step, width):
|
|
optimal = minbpm
|
|
optimal_acc = 0
|
|
accuracy = 0
|
|
|
|
bpmlst = []
|
|
scores = []
|
|
|
|
for beat in range(minbpm, maxbpm+step, step):
|
|
loopturn = 0
|
|
print("testing", beat)
|
|
accuracy = 0
|
|
current = 0
|
|
|
|
while(current+width < len(data)):
|
|
loopturn += 1
|
|
for o in range(-width, width+1):
|
|
accuracy += data[current + o]
|
|
#current = (loopturn*sample_rate)//beat
|
|
current += (sample_rate)//beat
|
|
|
|
#accuracy = accuracy/loopturn
|
|
|
|
#accuracy *= (1+(maxbpm-beat)/minbpm)
|
|
if optimal_acc < accuracy:
|
|
optimal_acc = accuracy
|
|
optimal = beat
|
|
bpmlst.append(beat)
|
|
scores.append(accuracy)
|
|
|
|
if(False):
|
|
plt.plot(bpmlst, scores)
|
|
plt.xlabel("BPM")
|
|
plt.ylabel("Score")
|
|
plt.grid()
|
|
plt.show()
|
|
|
|
return (optimal, optimal_acc)
|
|
'''
|
|
|
|
|
|
|
|
'''
|
|
def void_freq(song_name, offset, songlen, increment, lthr, gthr):
|
|
to_cut = 20000//2500
|
|
global_Zxx = np.array([])
|
|
global_f = np.array([])
|
|
global_t = np.array([])
|
|
current_time = offset
|
|
k = 0
|
|
sample_rate, global_data = wavfile.read(song_name)
|
|
blit = int(sample_rate*increment)
|
|
print("Blit :", blit)
|
|
while(current_time <= songlen):
|
|
#subprocess.run(["ffmpeg", "-ss", str(current_time), "-t", str(increment), "-i", song_name, "crop.wav"])
|
|
|
|
#sample_rate, audio_data = wavfile.read('crop.wav')
|
|
audio_data = global_data[int(k*blit):int((k+1)*blit)]
|
|
size = audio_data.size
|
|
|
|
#subprocess.run(["clear"])
|
|
#subprocess.run(["rm", "crop.wav"])
|
|
|
|
# do stuff here
|
|
#f, t, Zxx = signal.stft(audio_data, sample_rate, nperseg=1000)
|
|
f, t, Zxx = signal.spectrogram(audio_data, fs=sample_rate, nfft=size)
|
|
leng = len(f)
|
|
|
|
f, Zxx = f[:leng//to_cut], Zxx[:leng//to_cut]
|
|
|
|
for i in range(len(Zxx)):
|
|
for j in range(len(Zxx[i])):
|
|
#Zxx[i][j] *= 1127*np.log(1+f[i]/700)
|
|
Zxx[i][j] *= 1000
|
|
|
|
t = np.array([current_time + x for x in t])
|
|
|
|
if(k == 0):
|
|
global_f = f
|
|
global_t = t
|
|
global_Zxx = Zxx
|
|
else:
|
|
global_Zxx = np.concatenate((global_Zxx, Zxx), axis=1)
|
|
global_t = np.concatenate((global_t, t))
|
|
|
|
#print(len(global_t))
|
|
|
|
k += 1
|
|
current_time = offset + k*increment
|
|
|
|
print("Completion rate : ", np.round(100*(current_time-offset)/(songlen-offset), 4), "%")
|
|
|
|
print("Finding global max...")
|
|
gmax = 0
|
|
for i in range(len(global_Zxx)):
|
|
for j in range(len(global_Zxx[i])):
|
|
if(global_Zxx[i][j] > gmax):
|
|
gmax = global_Zxx[i][j]
|
|
|
|
print("Trimming...")
|
|
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):
|
|
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")
|
|
'''
|