aT.extract_features_and_train([root_data_path + "1/", root_data_path + "2/"],
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)
def main(argv):
if argv[1] == "-shortTerm":
for i in range(nExp):
[Fs, x] = audioBasicIO.read_audio_file("diarizationExample.wav")
duration = x.shape[0] / float(Fs)
t1 = time.time()
F = MidTermFeatures.short_term_feature_extraction(
x, Fs, 0.050 * Fs, 0.050 * Fs)
t2 = time.time()
perTime1 = duration / (t2 - t1)
print "short-term feature extraction: {0:.1f} x realtime".format(
perTime1)
elif argv[1] == "-classifyFile":
for i in range(nExp):
[Fs, x] = audioBasicIO.read_audio_file("diarizationExample.wav")
duration = x.shape[0] / float(Fs)
t1 = time.time()
aT.file_classification("diarizationExample.wav", "svmSM", "svm")
t2 = time.time()
perTime1 = duration / (t2 - t1)
print "Mid-term feature extraction + classification \t {0:.1f} x realtime".format(
perTime1)
elif argv[1] == "-mtClassify":
for i in range(nExp):
[Fs, x] = audioBasicIO.read_audio_file("diarizationExample.wav")
duration = x.shape[0] / float(Fs)
t1 = time.time()
[flagsInd, classesAll,
acc] = aS.mid_term_file_classification("diarizationExample.wav",
"svmSM", "svm", False, '')
t2 = time.time()
perTime1 = duration / (t2 - t1)
print "Fix-sized classification - segmentation \t {0:.1f} x realtime".format(
perTime1)
elif argv[1] == "-hmmSegmentation":
for i in range(nExp):
[Fs, x] = audioBasicIO.read_audio_file("diarizationExample.wav")
duration = x.shape[0] / float(Fs)
t1 = time.time()
aS.hmm_segmentation('diarizationExample.wav', 'hmmRadioSM', False,
'')
t2 = time.time()
perTime1 = duration / (t2 - t1)
print "HMM-based classification - segmentation \t {0:.1f} x realtime".format(
perTime1)
elif argv[1] == "-silenceRemoval":
for i in range(nExp):
[Fs, x] = audioBasicIO.read_audio_file("diarizationExample.wav")
duration = x.shape[0] / float(Fs)
t1 = time.time()
[Fs, x] = audioBasicIO.read_audio_file("diarizationExample.wav")
segments = aS.silence_removal(x,
Fs,
0.050,
0.050,
smooth_window=1.0,
Weight=0.3,
plot=False)
t2 = time.time()
perTime1 = duration / (t2 - t1)
print "Silence removal \t {0:.1f} x realtime".format(perTime1)
elif argv[1] == "-thumbnailing":
for i in range(nExp):
[Fs1, x1] = audioBasicIO.read_audio_file("scottish.wav")
duration1 = x1.shape[0] / float(Fs1)
t1 = time.time()
[A1, A2, B1, B2,
Smatrix] = aS.music_thumbnailing(x1, Fs1, 1.0, 1.0,
15.0) # find thumbnail endpoints
t2 = time.time()
perTime1 = duration1 / (t2 - t1)
print "Thumbnail \t {0:.1f} x realtime".format(perTime1)
elif argv[1] == "-diarization-noLDA":
for i in range(nExp):
[Fs1, x1] = audioBasicIO.read_audio_file("diarizationExample.wav")
duration1 = x1.shape[0] / float(Fs1)
t1 = time.time()
aS.speaker_diarization("diarizationExample.wav",
4,
LDAdim=0,
PLOT=False)
t2 = time.time()
perTime1 = duration1 / (t2 - t1)
print "Diarization \t {0:.1f} x realtime".format(perTime1)
elif argv[1] == "-diarization-LDA":
for i in range(nExp):
[Fs1, x1] = audioBasicIO.read_audio_file("diarizationExample.wav")
duration1 = x1.shape[0] / float(Fs1)
t1 = time.time()
aS.speaker_diarization("diarizationExample.wav", 4, PLOT=False)
t2 = time.time()
perTime1 = duration1 / (t2 - t1)
print "Diarization \t {0:.1f} x realtime".format(perTime1)
def segmentclassifyFileWrapperHMM(wavFile, hmmModelName):
gtFile = wavFile.replace(".wav", ".segments")
aS.hmm_segmentation(wavFile,
hmmModelName,
plot_results=True,
gt_file=gtFile)