def segmentclassifySMFileWrapper(inputWavFile, model_name, model_type):
if not os.path.isfile(model_name):
raise Exception("Input model_name not found!")
if not os.path.isfile(inputWavFile):
raise Exception("Input audio file not found!")
gtFile = ""
if inputWavFile[-4::]==".wav":
gtFile = inputWavFile.replace(".wav", ".segments")
if inputWavFile[-4::]==".mp3":
gtFile = inputWavFile.replace(".mp3", ".segments")
aS.mid_term_file_classification(inputWavFile, model_name, model_type, True, gtFile)
def test(audiofile_path):
model_path = r"models/svm_male_female"
model_type = "svm_rbf"
plot_results = False
labels, class_names, mt_step, class_probabilities = mid_term_file_classification(
audiofile_path, model_path, model_type, plot_results)
print("labels: ", len(labels))
# print "merged" segments (use labels_to_segments())
# print("\nSegments:")
segs, c, probs = labels_to_segments(labels, class_probabilities, mt_step)
# print("segs: ", len(segs))
print("prob test: ", len(probs))
# for iS, seg in enumerate(segs):
# if probs[iS] > 0.6:
# print(f'segment {iS} {seg[0]} sec - {seg[1]} sec: {class_names[int(c[iS])]} pro: {probs[iS]}')
# print("type(segs): ", type(segs))
# print("type(probs): ", type(probs))
return probs, segs.tolist(), class_names, c
# if __name__ == "__main__":
#
# audiofile_path = r"/Users/taanhtuan/Desktop/workproject/basic_audio_analysis-master/data/test.mp3"
# model_path = r"models/svm_male_female"
# model_type = "svm_rbf"
# plot_results = False
#
# labels, class_names, mt_step, class_probabilities = mid_term_file_classification(audiofile_path, model_path,
# model_type, plot_results)
#
# print("labels: ", len(labels))
# # print "merged" segments (use labels_to_segments())
# print("\nSegments:")
# segs, c, probs = labels_to_segments(labels, class_probabilities, mt_step)
#
# print("segs: ", len(segs))
# print("prob: ", len(probs))
#
# for iS, seg in enumerate(segs):
# if probs[iS] > 0.58:
# print(f'segment {iS} {seg[0]} sec - {seg[1]} sec: {class_names[int(c[iS])]} pro: {probs[iS]}')
"""!
@brief Example 31B
@details: Speech music discrimination and segmentation (using a trained
speech - music segment classifier)
Important: Need to run 31A first to extract speech music model (stored
in svm_speech_music)
@author Theodoros Giannakopoulos {[email protected]}
"""
from pyAudioAnalysis.audioSegmentation import mid_term_file_classification
if __name__ == '__main__':
au = "../data/scottish_radio.wav"
gt = "../data/scottish_radio.segments"
# au = "../data/musical_genres_small/hiphop/run_dmc_peter_riper.wav"
mid_term_file_classification(au, "svm_speech_music", "svm_rbf", True, gt)
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)
from pyAudioAnalysis import audioSegmentation as aS
[flagsInd, classesAll, acc,
CM] = aS.mid_term_file_classification("303.wav", "data/models/svm_rbf_sm",
"svm", True, 'data/scottish.segments')
x, Fs, round(Fs * 0.040), round(Fs * 0.040), 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 + "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
def test_mt_file_classification():
labels, class_names, accuracy, cm = aS.mid_term_file_classification(
"test_data/scottish.wav", "test_data/svm_rbf_sm", "svm_rbf", False,
"test_data/scottish.segments")
assert accuracy > 0.95, "Segment-level classification accuracy is low"
# "iNNovationMerge DailyCodeHub"
# Theme : Audio segmentation week with Python
# Fix-sized audio segmentation using pretrained eight class SVM model(svm_rbf_movie8class)
from pyAudioAnalysis import audioSegmentation as aS
[flagsInd, classesAll, acc, CM] = aS.mid_term_file_classification("data/scottish.wav", "data/models/svm_rbf_movie8class", "svm", True, 'data/scottish.segments')