def trainHMMsegmenter_fromfile(wavFile, gtFile, hmmModelName, mt_win, mt_step): if not os.path.isfile(wavFile): print("Error: wavfile does not exist!") return if not os.path.isfile(gtFile): print("Error: groundtruth does not exist!") return aS.trainHMM_fromFile(wavFile, gtFile, hmmModelName, mt_win, mt_step)
def trainHMMsegmenter_fromfile(wavFile, gtFile, hmmModelName, mt_win, mt_step): if not os.path.isfile(wavFile): print("Error: wavfile does not exist!") return if not os.path.isfile(gtFile): print("Error: groundtruth does not exist!") return aS.trainHMM_fromFile(wavFile, gtFile, hmmModelName, mt_win, mt_step)
specgram, TimeAxis, FreqAxis = audioFeatureExtraction.stChromagram( x, Fs, round(Fs * 0.040), round(Fs * 0.040), True) print("\n\n\n * * * TEST 4 * * * \n\n\n") aT.featureAndTrain([root_data_path + "SM/speech", root_data_path + "SM/music"], 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.mtFileClassification( root_data_path + "pyAudioAnalysis/data//scottish.wav", root_data_path + "pyAudioAnalysis/data/svmSM", "svm", True, root_data_path + 'pyAudioAnalysis/data/scottish.segments') print("\n\n\n * * * TEST 6 * * * \n\n\n") aS.trainHMM_fromFile(root_data_path + 'radioFinal/train/bbc4A.wav', root_data_path + 'radioFinal/train/bbc4A.segments', 'hmmTemp1', 1.0, 1.0) aS.trainHMM_fromDir(root_data_path + 'radioFinal/small', 'hmmTemp2', 1.0, 1.0) aS.hmmSegmentation(root_data_path + 'pyAudioAnalysis/data//scottish.wav', 'hmmTemp1', True, root_data_path + 'pyAudioAnalysis/data//scottish.segments') # test 1 aS.hmmSegmentation(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.featureAndTrainRegression(root_data_path + "pyAudioAnalysis/data/speechEmotion", 1, 1, 0.050,
x = audioBasicIO.stereo2mono(x) specgram, TimeAxis, FreqAxis = audioFeatureExtraction.stSpectogram(x, Fs, round(Fs * 0.040), round(Fs * 0.040), True) print("\n\n\n * * * TEST 3 * * * \n\n\n") [Fs, x] = audioBasicIO.readAudioFile(root_data_path + "pyAudioAnalysis/data/doremi.wav") x = audioBasicIO.stereo2mono(x) specgram, TimeAxis, FreqAxis = audioFeatureExtraction.stChromagram(x, Fs, round(Fs * 0.040), round(Fs * 0.040), True) print("\n\n\n * * * TEST 4 * * * \n\n\n") aT.featureAndTrain([root_data_path +"SM/speech",root_data_path + "SM/music"], 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.mtFileClassification(root_data_path + "pyAudioAnalysis/data//scottish.wav", root_data_path + "pyAudioAnalysis/data/svmSM", "svm", True, root_data_path + 'pyAudioAnalysis/data/scottish.segments') print("\n\n\n * * * TEST 6 * * * \n\n\n") aS.trainHMM_fromFile(root_data_path + 'radioFinal/train/bbc4A.wav', root_data_path + 'radioFinal/train/bbc4A.segments', 'hmmTemp1', 1.0, 1.0) aS.trainHMM_fromDir(root_data_path + 'radioFinal/small', 'hmmTemp2', 1.0, 1.0) aS.hmmSegmentation(root_data_path + 'pyAudioAnalysis/data//scottish.wav', 'hmmTemp1', True, root_data_path + 'pyAudioAnalysis/data//scottish.segments') # test 1 aS.hmmSegmentation(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.featureAndTrainRegression(root_data_path + "pyAudioAnalysis/data/speechEmotion", 1, 1, 0.050, 0.050, "svm_rbf", "temp.mod", compute_beat=False) print(aT.fileRegression(root_data_path + "pyAudioAnalysis/data/speechEmotion/01.wav", "temp.mod", "svm_rbf")) print("\n\n\n * * * TEST 8 * * * \n\n\n") aT.featureAndTrainRegression(root_data_path + "pyAudioAnalysis/data/speechEmotion", 1, 1, 0.050, 0.050, "svm", "temp.mod", compute_beat=False) print(aT.fileRegression(root_data_path + "pyAudioAnalysis/data/speechEmotion/01.wav", "temp.mod", "svm")) print("\n\n\n * * * TEST 9 * * * \n\n\n") aT.featureAndTrainRegression(root_data_path + "pyAudioAnalysis/data/speechEmotion", 1, 1, 0.050, 0.050, "randomforest", "temp.mod", compute_beat=False) print(aT.fileRegression(root_data_path + "pyAudioAnalysis/data/speechEmotion/01.wav", "temp.mod", "randomforest"))