File size: 1,687 Bytes
a8cb0a6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 |
# from rajatsLibrary.audio import AudioManipulator
# import librosa
# import matplotlib.pyplot as plt
# import numpy as np
# testAudio, _ = librosa.load('Base sounds correct.m4a')
# print(len(testAudio))
# # testAudio = testAudio[:6000]
# # audios = []
# # for i in range(24):
# # audios.extend(AudioManipulator.shiftPitchOfAudioValues(testAudio, _, i))
# # cleanAudio = []
# # noiceOnce = testAudio[:20000]
# # mxNoice = max(abs(noiceOnce))
# # for audioPoint in testAudio:
# # if(abs(audioPoint) > mxNoice):
# # cleanAudio.append(audioPoint)
# # else:
# # cleanAudio.append(0)
# # cleanAudio = cleanAudio[20000:]
# # cleanAudio = cleanAudio[:-20000]
# # noice = []
# # while len(noice) < len(testAudio):
# # noice.extend(noiceOnce)
# # print(len(noice))
# # testAudio = testAudio - noice[:len(testAudio)]
# plt.plot(testAudio)
# # plt.xticks(np.arange(0, len(testAudio)+1, 16000.0))
# plt.grid()
# plt.show()
# import os
# import librosa
# import matplotlib.pyplot as plt
# audios = []
# instruments = os.listdir('Instruments copy')
# instruments = sorted(instruments)
# instruments1 = instruments[:7]
# instruments2 = instruments[7:]
# instruments2.extend(instruments1)
# instruments = instruments2
# print(instruments)
# scaling_factors = [0.92, 1.94, 3.16, 1.41, 0.633, 0.775, 3.954, 1.712, 0.861, 1.327, 3.768, 1.013, 2.307, 0.645, 10.482, 1.255]
# i = 0
# for instrument in instruments:
# audioValues, _ = librosa.load('Instruments copy/' + instrument)
# audioValues = audioValues * scaling_factors[i]
# audios.extend(audioValues)
# audios.extend([0] * 10000)
# i+=1
# plt.plot(audios)
# plt.grid()
# plt.show() |