pyAudioAnalysis/tests/script_tests.py at master · justinbacle/pyAudioAnalysis · GitHub

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from __future__ import print_function
import sys
import os
sys.path.insert(0, os.path.join(os.path.dirname(
    os.path.realpath(__file__)), "../"))
from pyAudioAnalysis import audioBasicIO
from pyAudioAnalysis import ShortTermFeatures
from pyAudioAnalysis import audioTrainTest as aT
from pyAudioAnalysis import audioSegmentation as aS
import matplotlib.pyplot as plt

root_data_path = "/Users/tyiannak/ResearchData/Audio Dataset/pyAudioAnalysisData/"

print("\n\n\n * * * TEST 1 * * * \n\n\n")
[Fs, x] = audioBasicIO.read_audio_file(root_data_path + "pyAudioAnalysis/data/count.wav");
F, f_names = ShortTermFeatures.feature_extraction(x, Fs, 0.050 * Fs, 0.025 * Fs);
plt.subplot(2,1,1); plt.plot(F[0,:]); plt.xlabel('Frame no'); plt.ylabel(f_names[0]);
plt.subplot(2,1,2); plt.plot(F[1,:]); plt.xlabel('Frame no'); plt.ylabel(f_names[1]); plt.show()

print("\n\n\n * * * TEST 2 * * * \n\n\n")
[Fs, x] = audioBasicIO.read_audio_file(root_data_path + "pyAudioAnalysis/data/doremi.wav")
x = audioBasicIO.stereo_to_mono(x)
specgram, TimeAxis, FreqAxis = ShortTermFeatures.spectrogram(x, Fs, round(Fs * 0.040), round(Fs * 0.040), True, True)

print("\n\n\n * * * TEST 3 * * * \n\n\n")
[Fs, x] = audioBasicIO.read_audio_file(root_data_path + "pyAudioAnalysis/data/doremi.wav")
x = audioBasicIO.stereo_to_mono(x)
specgram, TimeAxis, FreqAxis = ShortTermFeatures.chromagram(x, Fs, round(Fs * 0.040), round(Fs * 0.040), True)

print("\n\n\n * * * TEST 4 * * * \n\n\n")
aT.extract_features_and_train([root_data_path + "speakerAll/F1/", root_data_path + "speakerAll/F2/"], 1.0, 1.0, 0.2, 0.2, "svm", "temp", True)

print("\n\n\n * * * TEST 5 * * * \n\n\n")
[flagsInd, classesAll, acc, CM] = aS.mid_term_file_classification(root_data_path + "scottish.wav", root_data_path + "models/svm_rbf_sm", "svm_rbf", True, root_data_path + 'pyAudioAnalysis/data/scottish.segments')

print("\n\n\n * * * TEST 6 * * * \n\n\n")
aS.train_hmm_from_file(root_data_path + 'radioFinal/train/bbc4A.wav', root_data_path + 'radioFinal/train/bbc4A.segments', 'hmmTemp1', 1.0, 1.0)
aS.train_hmm_from_directory(root_data_path + 'radioFinal/small', 'hmmTemp2', 1.0, 1.0)
aS.hmm_segmentation(root_data_path + 'pyAudioAnalysis/data//scottish.wav', 'hmmTemp1', True, root_data_path + 'pyAudioAnalysis/data//scottish.segments')				# test 1
aS.hmm_segmentation(root_data_path + 'pyAudioAnalysis/data//scottish.wav', 'hmmTemp2', True, root_data_path + 'pyAudioAnalysis/data//scottish.segments')				# test 2

print("\n\n\n * * * TEST 7 * * * \n\n\n")
aT.feature_extraction_train_regression(root_data_path + "pyAudioAnalysis/data/speechEmotion", 1, 1, 0.050, 0.050, "svm_rbf", "temp.mod", compute_beat=False)
print(aT.file_regression(root_data_path + "pyAudioAnalysis/data/speechEmotion/01.wav", "temp.mod", "svm_rbf"))

print("\n\n\n * * * TEST 8 * * * \n\n\n")
aT.feature_extraction_train_regression(root_data_path + "pyAudioAnalysis/data/speechEmotion", 1, 1, 0.050, 0.050, "svm", "temp.mod", compute_beat=False)
print(aT.file_regression(root_data_path + "pyAudioAnalysis/data/speechEmotion/01.wav", "temp.mod", "svm"))

print("\n\n\n * * * TEST 9 * * * \n\n\n")
aT.feature_extraction_train_regression(root_data_path + "pyAudioAnalysis/data/speechEmotion", 1, 1, 0.050, 0.050, "randomforest", "temp.mod", compute_beat=False)
print(aT.file_regression(root_data_path + "pyAudioAnalysis/data/speechEmotion/01.wav", "temp.mod", "randomforest"))