def classifyFolderWrapper(inputFolder, modelType, modelName, outputMode=False):
if not os.path.isfile(modelName):
raise Exception("Input modelName not found!")
files = "*.wav"
if os.path.isdir(inputFolder):
strFilePattern = os.path.join(inputFolder, files)
else:
strFilePattern = inputFolder + files
wavFilesList = []
wavFilesList.extend(glob.glob(strFilePattern))
wavFilesList = sorted(wavFilesList)
if len(wavFilesList) == 0:
print "No WAV files found!"
return
Results = []
for wavFile in wavFilesList:
[Result, P,
classNames] = aT.fileClassification(wavFile, modelName, modelType)
Result = int(Result)
Results.append(Result)
if outputMode:
print "{0:s}\t{1:s}".format(wavFile, classNames[Result])
Results = numpy.array(Results)
# print distribution of classes:
[Histogram, _] = numpy.histogram(Results,
bins=numpy.arange(len(classNames) + 1))
for i, h in enumerate(Histogram):
print "{0:20s}\t\t{1:d}".format(classNames[i], h)
def classifyFolderWrapper(inputFolder, modelType, modelName, outputMode=False):
if not os.path.isfile(modelName):
raise Exception("Input modelName not found!")
files = "*.wav"
if os.path.isdir(inputFolder):
strFilePattern = os.path.join(inputFolder, files)
else:
strFilePattern = inputFolder + files
wavFilesList = []
wavFilesList.extend(glob.glob(strFilePattern))
wavFilesList = sorted(wavFilesList)
if len(wavFilesList) == 0:
print "No WAV files found!"
return
Results = []
for wavFile in wavFilesList:
[Result, P, classNames] = aT.fileClassification(wavFile, modelName, modelType)
Result = int(Result)
Results.append(Result)
if outputMode:
print "{0:s}\t{1:s}".format(wavFile, classNames[Result])
Results = numpy.array(Results)
# print distribution of classes:
[Histogram, _] = numpy.histogram(Results, bins=numpy.arange(len(classNames) + 1))
for i, h in enumerate(Histogram):
print "{0:20s}\t\t{1:d}".format(classNames[i], h)
def classifyFolderWrapper(inputFolder, modelType, modelName, outputMode=False):
if not os.path.isfile(modelName):
raise Exception("Input modelName not found!")
types = ('*.wav', '*.aif', '*.aiff', '*.mp3')
wavFilesList = []
for files in types:
wavFilesList.extend(glob.glob((inputFolder + files)))
wavFilesList = sorted(wavFilesList)
if len(wavFilesList) == 0:
print("No WAV files found!")
return
Results = []
for wavFile in wavFilesList:
[Result, P,
classNames] = aT.fileClassification(wavFile, modelName, modelType)
Result = int(Result)
Results.append(Result)
if outputMode:
print("{0:s}\t{1:s}".format(wavFile, classNames[Result]))
Results = numpy.array(Results)
# print distribution of classes:
[Histogram, _] = numpy.histogram(Results,
bins=numpy.arange(len(classNames) + 1))
for i, h in enumerate(Histogram):
print("{0:20s}\t\t{1:d}".format(classNames[i], h))
def classify_file(recording, model_name): Result, P, classNames = aT.fileClassification(recording, model_path(model_name), "svm") winner_class = classNames[int(Result)] winner_prob =P[int(Result)] print "Winner class: " + winner_class + " - with prob: " + str(winner_prob) return (winner_class, winner_prob)
def classifyFileWrapper(inputFile, modelType, modelName):
if not os.path.isfile(modelName):
raise Exception("Input modelName not found!")
if not os.path.isfile(inputFile):
raise Exception("Input audio file not found!")
[Result, P, classNames] = aT.fileClassification(inputFile, modelName, modelType)
print "{0:s}\t{1:s}".format("Class", "Probability")
for i, c in enumerate(classNames):
print "{0:s}\t{1:.2f}".format(c, P[i])
print "Winner class: " + classNames[int(Result)]
def classifyFolderWrapper(inputFolder, modelType, modelName, outputMode=False):
if not os.path.isfile(modelName):
raise Exception("Input modelName not found!")
if modelType=='svm':
[Classifier, MEAN, STD, classNames, mtWin, mtStep, stWin, stStep, computeBEAT] = aT.loadSVModel(modelName)
elif modelType=='knn':
[Classifier, MEAN, STD, classNames, mtWin, mtStep, stWin, stStep, computeBEAT] = aT.loadKNNModel(modelName)
PsAll = numpy.zeros((len(classNames), ))
files = "*.wav"
if os.path.isdir(inputFolder):
strFilePattern = os.path.join(inputFolder, files)
else:
strFilePattern = inputFolder + files
wavFilesList = []
wavFilesList.extend(glob.glob(strFilePattern))
wavFilesList = sorted(wavFilesList)
if len(wavFilesList)==0:
print "No WAV files found!"
return
Results = []
for wavFile in wavFilesList:
[Fs, x] = audioBasicIO.readAudioFile(wavFile)
signalLength = x.shape[0] / float(Fs)
[Result, P, classNames] = aT.fileClassification(wavFile, modelName, modelType)
PsAll += (numpy.array(P) * signalLength)
Result = int(Result)
Results.append(Result)
if outputMode:
print "{0:s}\t{1:s}".format(wavFile,classNames[Result])
Results = numpy.array(Results)
# print distribution of classes:
[Histogram, _] = numpy.histogram(Results, bins=numpy.arange(len(classNames)+1))
if outputMode:
for i,h in enumerate(Histogram):
print "{0:20s}\t\t{1:d}".format(classNames[i], h)
PsAll = PsAll / numpy.sum(PsAll)
if outputMode:
fig = plt.figure()
ax = fig.add_subplot(111)
plt.title("Classes percentage " + inputFolder.replace('Segments',''))
ax.axis((0, len(classNames)+1, 0, 1))
ax.set_xticks(numpy.array(range(len(classNames)+1)))
ax.set_xticklabels([" "] + classNames)
ax.bar(numpy.array(range(len(classNames)))+0.5, PsAll)
plt.show()
return classNames, PsAll
def classifyFileWrapper(inputFile, modelType, modelName):
if not os.path.isfile(modelName):
raise Exception("Input modelName not found!")
if not os.path.isfile(inputFile):
raise Exception("Input audio file not found!")
[Result, P, classNames] = aT.fileClassification(inputFile, modelName, modelType)
print "{0:s}\t{1:s}".format("Class", "Probability")
for i, c in enumerate(classNames):
print "{0:s}\t{1:.2f}".format(c, P[i])
print "Winner class: " + classNames[int(Result)]
def classifyFolderWrapper(inputFolder, modelType, modelName, outputMode=False):
if not os.path.isfile(modelName):
raise Exception("Input modelName not found!")
if modelType=='svm':
[Classifier, MEAN, STD, classNames, mtWin, mtStep, stWin, stStep, computeBEAT] = aT.loadSVModel(modelName)
elif modelType=='knn':
[Classifier, MEAN, STD, classNames, mtWin, mtStep, stWin, stStep, computeBEAT] = aT.loadKNNModel(modelName)
PsAll = numpy.zeros((len(classNames), ))
files = "*.wav"
if os.path.isdir(inputFolder):
strFilePattern = os.path.join(inputFolder, files)
else:
strFilePattern = inputFolder + files
wavFilesList = []
wavFilesList.extend(glob.glob(strFilePattern))
wavFilesList = sorted(wavFilesList)
if len(wavFilesList)==0:
print "No WAV files found!"
return
Results = []
for wavFile in wavFilesList:
[Fs, x] = audioBasicIO.readAudioFile(wavFile)
signalLength = x.shape[0] / float(Fs)
[Result, P, classNames] = aT.fileClassification(wavFile, modelName, modelType)
PsAll += (numpy.array(P) * signalLength)
Result = int(Result)
Results.append(Result)
if outputMode:
print "{0:s}\t{1:s}".format(wavFile,classNames[Result])
Results = numpy.array(Results)
# print distribution of classes:
[Histogram, _] = numpy.histogram(Results, bins=numpy.arange(len(classNames)+1))
if outputMode:
for i,h in enumerate(Histogram):
print "{0:20s}\t\t{1:d}".format(classNames[i], h)
PsAll = PsAll / numpy.sum(PsAll)
if outputMode:
fig = plt.figure()
ax = fig.add_subplot(111)
plt.title("Classes percentage " + inputFolder.replace('Segments',''))
ax.axis((0, len(classNames)+1, 0, 1))
ax.set_xticks(numpy.array(range(len(classNames)+1)))
ax.set_xticklabels([" "] + classNames)
ax.bar(numpy.array(range(len(classNames)))+0.5, PsAll)
plt.show()
return classNames, PsAll
def main(argv):
if argv[1] == "-dirMp3toWAV": # convert mp3 to wav (batch)
if len(argv) == 5:
path = argv[2]
if argv[3] not in ["8000", "16000", "32000", "44100"]:
print "Error. Unsupported sampling rate (must be: 8000, 16000, 32000 or 44100)."
return
if argv[4] not in ["1", "2"]:
print "Error. Number of output channels must be 1 or 2"
return
if not os.path.isdir(path):
raise Exception("Input path not found!")
useMp3TagsAsNames = True
audioBasicIO.convertDirMP3ToWav(path, int(argv[3]), int(argv[4]),
useMp3TagsAsNames)
else:
print "Error.\nSyntax: " + argv[
0] + " -dirMp3toWAV <dirName> <sampling Freq> <numOfChannels>"
if argv[1] == "-dirWAVChangeFs": # convert mp3 to wav (batch)
if len(argv) == 5:
path = argv[2]
if argv[3] not in ["8000", "16000", "32000", "44100"]:
print "Error. Unsupported sampling rate (must be: 8000, 16000, 32000 or 44100)."
return
if argv[4] not in ["1", "2"]:
print "Error. Number of output channels must be 1 or 2"
return
if not os.path.isdir(path):
raise Exception("Input path not found!")
audioBasicIO.convertFsDirWavToWav(path, int(argv[3]), int(argv[4]))
else:
print "Error.\nSyntax: " + argv[
0] + " -dirMp3toWAV <dirName> <sampling Freq> <numOfChannels>"
elif argv[
1] == "-featureExtractionFile": # short-term and mid-term feature extraction to files (csv and numpy)
if len(argv) == 7:
wavFileName = argv[2]
if not os.path.isfile(wavFileName):
raise Exception("Input audio file not found!")
if not (uT.isNum(argv[3]) and uT.isNum(argv[4])
and uT.isNum(argv[5]) and uT.isNum(argv[6])):
raise Exception(
"Mid-term and short-term window sizes and steps must be numbers!"
)
mtWin = float(argv[3])
mtStep = float(argv[4])
stWin = float(argv[5])
stStep = float(argv[6])
outFile = wavFileName
aF.mtFeatureExtractionToFile(wavFileName, mtWin, mtStep, stWin,
stStep, outFile, True, True, True)
else:
print "Error.\nSyntax: " + argv[
0] + " -featureExtractionFile <wavFileName> <mtWin> <mtStep> <stWin> <stStep>"
elif argv[1] == "-beatExtraction":
if len(argv) == 4:
wavFileName = argv[2]
if not os.path.isfile(wavFileName):
raise Exception("Input audio file not found!")
if not (uT.isNum(argv[3])):
raise Exception("PLOT must be either 0 or 1")
if not ((int(argv[3]) == 0) or (int(argv[3]) == 1)):
raise Exception("PLOT must be either 0 or 1")
[Fs, x] = audioBasicIO.readAudioFile(wavFileName)
F = aF.stFeatureExtraction(x, Fs, 0.050 * Fs, 0.050 * Fs)
BPM, ratio = aF.beatExtraction(F, 0.050, int(argv[3]) == 1)
print "Beat: {0:d} bpm ".format(int(BPM))
print "Ratio: {0:.2f} ".format(ratio)
else:
print "Error.\nSyntax: " + argv[
0] + " -beatExtraction <wavFileName> <PLOT (0 or 1)>"
elif argv[
1] == '-featureExtractionDir': # same as -featureExtractionFile, in a batch mode (i.e. for each WAV file in the provided path)
if len(argv) == 7:
path = argv[2]
if not os.path.isdir(path):
raise Exception("Input path not found!")
if not (uT.isNum(argv[3]) and uT.isNum(argv[4])
and uT.isNum(argv[5]) and uT.isNum(argv[6])):
raise Exception(
"Mid-term and short-term window sizes and steps must be numbers!"
)
mtWin = float(argv[3])
mtStep = float(argv[4])
stWin = float(argv[5])
stStep = float(argv[6])
aF.mtFeatureExtractionToFileDir(path, mtWin, mtStep, stWin, stStep,
True, True, True)
else:
print "Error.\nSyntax: " + argv[
0] + " -featureExtractionDir <path> <mtWin> <mtStep> <stWin> <stStep>"
elif argv[
1] == '-featureVisualizationDir': # visualize the content relationships between recordings stored in a folder
if len(argv) == 3:
if not os.path.isdir(argv[2]):
raise Exception("Input folder not found!")
aV.visualizeFeaturesFolder(argv[2], "pca", "")
elif argv[
1] == '-fileSpectrogram': # show spectogram of a sound stored in a file
if len(argv) == 3:
wavFileName = argv[2]
if not os.path.isfile(wavFileName):
raise Exception("Input audio file not found!")
[Fs, x] = audioBasicIO.readAudioFile(wavFileName)
x = audioBasicIO.stereo2mono(x)
specgram, TimeAxis, FreqAxis = aF.stSpectogram(
x, Fs, round(Fs * 0.040), round(Fs * 0.040), True)
else:
print "Error.\nSyntax: " + argv[0] + " -fileSpectrogram <fileName>"
elif argv[
1] == '-fileChromagram': # show spectogram of a sound stored in a file
if len(argv) == 3:
wavFileName = argv[2]
if not os.path.isfile(wavFileName):
raise Exception("Input audio file not found!")
[Fs, x] = audioBasicIO.readAudioFile(wavFileName)
x = audioBasicIO.stereo2mono(x)
specgram, TimeAxis, FreqAxis = aF.stChromagram(
x, Fs, round(Fs * 0.040), round(Fs * 0.040), True)
else:
print "Error.\nSyntax: " + argv[0] + " -fileSpectrogram <fileName>"
elif argv[1] == "-trainClassifier": # Segment classifier training (OK)
if len(argv) > 6:
method = argv[2]
beatFeatures = (int(argv[3]) == 1)
listOfDirs = argv[4:len(argv) - 1]
modelName = argv[-1]
aT.featureAndTrain(listOfDirs,
1,
1,
aT.shortTermWindow,
aT.shortTermStep,
method.lower(),
modelName,
computeBEAT=beatFeatures)
else:
print "Error.\nSyntax: " + argv[
0] + " -trainClassifier <method(svm or knn)> <beat features> <directory 1> <directory 2> ... <directory N> <modelName>"
elif argv[1] == "-trainRegression": # Segment regression model
if len(argv) == 6:
method = argv[2]
beatFeatures = (int(argv[3]) == 1)
dirName = argv[4]
modelName = argv[5]
aT.featureAndTrainRegression(dirName,
1,
1,
aT.shortTermWindow,
aT.shortTermStep,
method.lower(),
modelName,
computeBEAT=beatFeatures)
else:
print "Error.\nSyntax: " + argv[
0] + " -trainRegression <method(svm or knn)> <beat features> <directory> <modelName>"
elif argv[1] == "-classifyFile": # Single File Classification (OK)
if len(argv) == 5:
modelType = argv[2]
modelName = argv[3]
inputFile = argv[4]
if modelType not in ["svm", "knn"]:
raise Exception("ModelType has to be either svm or knn!")
if not os.path.isfile(modelName):
raise Exception("Input modelName not found!")
if not os.path.isfile(inputFile):
raise Exception("Input audio file not found!")
[Result, P,
classNames] = aT.fileClassification(inputFile, modelName,
modelType)
print "{0:s}\t{1:s}".format("Class", "Probability")
for i, c in enumerate(classNames):
print "{0:s}\t{1:.2f}".format(c, P[i])
print "Winner class: " + classNames[int(Result)]
else:
print "Error.\nSyntax: " + argv[
0] + " -classifyFile <method(svm or knn)> <modelName> <fileName>"
elif argv[1] == "-regressionFile": # Single File Classification (OK)
if len(argv) == 5:
modelType = argv[2]
modelName = argv[3]
inputFile = argv[4]
if modelType not in ["svm", "knn"]:
raise Exception("ModelType has to be either svm or knn!")
if not os.path.isfile(inputFile):
raise Exception("Input audio file not found!")
R, regressionNames = aT.fileRegression(inputFile, modelName,
modelType)
for i in range(len(R)):
print "{0:s}\t{1:.3f}".format(regressionNames[i], R[i])
#print "{0:s}\t{1:.2f}".format(c,P[i])
else:
print "Error.\nSyntax: " + argv[
0] + " -regressionFile <method(svm or knn)> <modelName> <fileName>"
elif argv[1] == "-classifyFolder": # Directory classification (Ok)
if len(argv) == 6 or len(argv) == 5:
modelType = argv[2]
modelName = argv[3]
inputFolder = argv[4]
if len(argv) == 6:
outputMode = argv[5]
else:
outputMode = "0"
if modelType not in ["svm", "knn"]:
raise Exception("ModelType has to be either svm or knn!")
if outputMode not in ["0", "1"]:
raise Exception("outputMode has to be 0 or 1")
if not os.path.isfile(modelName):
raise Exception("Input modelName not found!")
files = '*.wav'
if os.path.isdir(inputFolder):
strFilePattern = os.path.join(inputFolder, files)
else:
strFilePattern = inputFolder + files
wavFilesList = []
wavFilesList.extend(glob.glob(strFilePattern))
wavFilesList = sorted(wavFilesList)
if len(wavFilesList) == 0:
print "No WAV files found!"
return
Results = []
for wavFile in wavFilesList:
[Result, P,
classNames] = aT.fileClassification(wavFile, modelName,
modelType)
Result = int(Result)
Results.append(Result)
if outputMode == "1":
print "{0:s}\t{1:s}".format(wavFile, classNames[Result])
Results = numpy.array(Results)
# print distribution of classes:
[Histogram,
_] = numpy.histogram(Results,
bins=numpy.arange(len(classNames) + 1))
for i, h in enumerate(Histogram):
print "{0:20s}\t\t{1:d}".format(classNames[i], h)
else:
print "Error.\nSyntax: " + argv[
0] + " -classifyFolder <method(svm or knn)> <modelName> <folderName> <outputMode(0 or 1)"
elif argv[
1] == "-regressionFolder": # Regression applied on the WAV files of a folder
if len(argv) == 5:
modelType = argv[2]
modelName = argv[3]
inputFolder = argv[4]
if modelType not in ["svm", "knn"]:
raise Exception("ModelType has to be either svm or knn!")
files = '*.wav'
if os.path.isdir(inputFolder):
strFilePattern = os.path.join(inputFolder, files)
else:
strFilePattern = inputFolder + files
wavFilesList = []
wavFilesList.extend(glob.glob(strFilePattern))
wavFilesList = sorted(wavFilesList)
if len(wavFilesList) == 0:
print "No WAV files found!"
return
Results = []
for wavFile in wavFilesList:
R, regressionNames = aT.fileRegression(wavFile, modelName,
modelType)
Results.append(R)
Results = numpy.array(Results)
for i, r in enumerate(regressionNames):
[Histogram, bins] = numpy.histogram(Results[:, i])
centers = (bins[0:-1] + bins[1::]) / 2.0
plt.subplot(len(regressionNames), 1, i)
plt.plot(centers, Histogram)
plt.title(r)
plt.show()
# for h in Histogram:
# print "{0:20d}".format(h),
# if outputMode=="1":
# for i,h in enumerate(Histogram):
# print "{0:20s}\t\t{1:d}".format(classNames[i], h)
else:
print "Error.\nSyntax: " + argv[
0] + " -regressionFolder <method(svm or knn)> <modelName> <folderName>"
elif argv[1] == '-trainHMMsegmenter_fromfile':
if len(argv) == 7:
wavFile = argv[2]
gtFile = argv[3]
hmmModelName = argv[4]
if not uT.isNum(argv[5]):
print "Error: mid-term window size must be float!"
return
if not uT.isNum(argv[6]):
print "Error: mid-term window step must be float!"
return
mtWin = float(argv[5])
mtStep = float(argv[6])
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, mtWin, mtStep)
else:
print "Error.\nSyntax: " + argv[
0] + " -trainHMMsegmenter_fromfile <wavFilePath> <gtSegmentFilePath> <hmmModelFileName> <mtWin> <mtStep>"
elif argv[1] == '-trainHMMsegmenter_fromdir':
if len(argv) == 6:
dirPath = argv[2]
hmmModelName = argv[3]
if not uT.isNum(argv[4]):
print "Error: mid-term window size must be float!"
if not uT.isNum(argv[5]):
print "Error: mid-term window step must be float!"
mtWin = float(argv[4])
mtStep = float(argv[5])
aS.trainHMM_fromDir(dirPath, hmmModelName, mtWin, mtStep)
else:
print "Error.\nSyntax: " + argv[
0] + " -trainHMMsegmenter_fromdir <dirPath> <hmmModelFileName> <mtWin> <mtStep>"
elif argv[
1] == "-segmentClassifyFileHMM": # HMM-based segmentation-classification
if len(argv) == 4:
hmmModelName = argv[2]
wavFile = argv[3]
gtFile = wavFile.replace('.wav', '.segments')
aS.hmmSegmentation(wavFile,
hmmModelName,
PLOT=True,
gtFileName=gtFile)
else:
print "Error.\nSyntax: " + argv[
0] + " -segmentClassifyHMM <hmmModelName> <fileName>"
elif argv[
1] == '-segmentClassifyFile': # Segmentation-classification (fix-sized segment using knn or svm)
if (len(argv) == 5):
modelType = argv[2]
modelName = argv[3]
inputWavFile = argv[4]
if modelType not in ["svm", "knn"]:
raise Exception("ModelType has to be either svm or knn!")
if not os.path.isfile(modelName):
raise Exception("Input modelName not found!")
if not os.path.isfile(inputWavFile):
raise Exception("Input audio file not found!")
gtFile = inputWavFile.replace('.wav', '.segments')
aS.mtFileClassification(inputWavFile, modelName, modelType, True,
gtFile)
else:
print "Error.\nSyntax: " + argv[
0] + " -segmentClassifyFile <method(svm or knn)> <modelName> <fileName>"
elif argv[1] == "-segmentationEvaluation":
if len(argv) == 5:
methodName = argv[2]
modelName = argv[3]
dirName = argv[4]
aS.evaluateSegmentationClassificationDir(dirName, modelName,
methodName)
else:
print "Error.\nSyntax: " + argv[
0] + " -segmentationEvaluation <method(svm or knn)> <modelName> <directoryName>"
elif argv[1] == "-silenceRemoval":
if len(argv) == 5:
inputFile = argv[2]
if not os.path.isfile(inputFile):
raise Exception("Input audio file not found!")
smoothingWindow = float(argv[3])
weight = float(argv[4])
[Fs,
x] = audioBasicIO.readAudioFile(inputFile) # read audio signal
segmentLimits = aS.silenceRemoval(x, Fs, 0.05, 0.05,
smoothingWindow, weight,
False) # get onsets
for i, s in enumerate(segmentLimits):
strOut = "{0:s}_{1:.3f}-{2:.3f}.wav".format(
inputFile[0:-4], s[0], s[1])
wavfile.write(strOut, Fs, x[int(Fs * s[0]):int(Fs * s[1])])
else:
print "Error.\nSyntax: " + argv[
0] + " -silenceRemoval <inputFile> <smoothinWindow(secs)> <Threshold Weight>"
elif argv[
1] == '-speakerDiarization': # speaker diarization (from file): TODO
inputFile = argv[2]
nSpeakers = int(argv[3])
useLDA = (int(argv[4]) == 1)
if useLDA:
aS.speakerDiarization(inputFile, nSpeakers, PLOT=True)
else:
aS.speakerDiarization(inputFile, nSpeakers, LDAdim=0, PLOT=True)
#print speechLimits
elif argv[1] == "-speakerDiarizationScriptEval":
dir = argv[2]
listOfLDAs = [int(l) for l in argv[3::]]
aS.speakerDiarizationEvaluateScript(dir, listOfLDAs)
elif argv[1] == '-thumbnail': # music thumbnailing (OK)
if len(argv) == 4:
inputFile = argv[2]
stWindow = 1.0
stStep = 1.0
if not os.path.isfile(inputFile):
raise Exception("Input audio file not found!")
[Fs, x] = audioBasicIO.readAudioFile(inputFile) # read file
if Fs == -1: # could not read file
return
try:
thumbnailSize = float(argv[3])
except ValueError:
print "Thumbnail size must be a float (in seconds)"
return
[A1, A2, B1, B2, Smatrix] = aS.musicThumbnailing(
x, Fs, stWindow, stStep,
thumbnailSize) # find thumbnail endpoints
# write thumbnails to WAV files:
thumbnailFileName1 = inputFile.replace(".wav", "_thumb1.wav")
thumbnailFileName2 = inputFile.replace(".wav", "_thumb2.wav")
wavfile.write(thumbnailFileName1, Fs, x[int(Fs * A1):int(Fs * A2)])
wavfile.write(thumbnailFileName2, Fs, x[int(Fs * B1):int(Fs * B2)])
print "1st thumbnail (stored in file {0:s}): {1:4.1f}sec -- {2:4.1f}sec".format(
thumbnailFileName1, A1, A2)
print "2nd thumbnail (stored in file {0:s}): {1:4.1f}sec -- {2:4.1f}sec".format(
thumbnailFileName2, B1, B2)
# Plot self-similarity matrix:
fig = plt.figure()
ax = fig.add_subplot(111, aspect='auto')
plt.imshow(Smatrix)
# Plot best-similarity diagonal:
Xcenter = (A1 / stStep + A2 / stStep) / 2.0
Ycenter = (B1 / stStep + B2 / stStep) / 2.0
e1 = matplotlib.patches.Ellipse((Ycenter, Xcenter),
thumbnailSize * 1.4,
3,
angle=45,
linewidth=3,
fill=False)
ax.add_patch(e1)
plt.plot([B1, Smatrix.shape[0]], [A1, A1],
color='k',
linestyle='--',
linewidth=2)
plt.plot([B2, Smatrix.shape[0]], [A2, A2],
color='k',
linestyle='--',
linewidth=2)
plt.plot([B1, B1], [A1, Smatrix.shape[0]],
color='k',
linestyle='--',
linewidth=2)
plt.plot([B2, B2], [A2, Smatrix.shape[0]],
color='k',
linestyle='--',
linewidth=2)
plt.xlim([0, Smatrix.shape[0]])
plt.ylim([Smatrix.shape[1], 0])
ax.yaxis.set_label_position("right")
ax.yaxis.tick_right()
plt.xlabel('frame no')
plt.ylabel('frame no')
plt.title('Self-similarity matrix')
plt.show()
else:
print "Error.\nSyntax: " + argv[
0] + " -thumbnail <filename> <thumbnailsize(seconds)>"
import matplotlib.pyplot as plt
import audioTrainTest as aT
plot = False
[Fs, x] = audioBasicIO.readAudioFile("data/Heavy.wav")
F = audioFeatureExtraction.stFeatureExtraction(x, Fs, 0.050 * Fs, 0.025 * Fs)
# ZCR: The rate of sign-changes of the signal during the duration of a particular frame.
if plot:
plt.subplot(2, 2, 1)
plt.plot(F[0, :])
plt.xlabel('Frame no')
plt.ylabel('ZCR')
plt.subplot(2, 2, 2)
plt.plot(F[1, :])
plt.xlabel('Frame no')
plt.ylabel('Energy')
plt.subplot(2, 2, 3)
plt.plot(F[2, :])
plt.xlabel('Frame no')
plt.ylabel('Entropy of Energy')
plt.subplot(2, 2, 4)
plt.plot(F[3, :])
plt.xlabel('Frame no')
plt.ylabel('Spectral Centroid')
plt.show()
Result, P, classNames = aT.fileClassification("data/Heavy.wav",
"data/svmMusicGenre3", "svm")
print Result
print P
print classNames
import sys
sys.path.append("/home/marta/pyAudioAnalysis/")
import audioTrainTest as aT
aT.featureAndTrain(["classifierData/music", "classifierData/speech"], 1.0, 1.0,
aT.shortTermWindow, aT.shortTermStep, "svm", "svmSMtemp",
False)
aT.fileClassification("data/doremi.wav", "svmSMtemp", "svm")
# import os
# import sys
# lib_path = os.path.abspath(os.path.join(__file__, '..', '..', '..', 'lib'))
# sys.path.append("/home/marta/pyAudioAnalysis/")
# import audioTrainTest
import sys
sys.path.insert(0, '/home/marta/pyAudioAnalysis/')
import audioTrainTest as aT
aT.featureAndTrain(["classifierData/music", "classifierData/speech"], 1.0, 1.0,
aT.shortTermWindow, aT.shortTermStep, "svm", "svmSMtemp",
False)
aT.fileClassification("data/doremi.wav", "svmSMtemp", "svm")
import pyAudioAnalysis
#import matplotlib.pyplot as plt
#import audioBasicIO as au
#import audioFeatureExtraction as afe
import audioTrainTest as aT
#[Fs,X] = au.readAudioFile('voice.wav')
#afe.mtFeatureExtractionToFileDir('/home/chocka', 1, 1, 0.050, 0.5)
#aT.featureAndTrain(["/home/chocka/humandata/Final_reading","/home/chocka/humandata/cycle" ,"/home/chocka/humandata/Blank"],1.0, 1.0, aT.shortTermWindow, aT.shortTermStep, "svm_rbf", "humancycle")
[Result, P, classNames] = aT.fileClassification("/home/chocka/cte.wav",
"humancycle", "svm_rbf")
#print (Result)
print(P)
print(classNames)
#source ./env/bin/activate
def main(argv):
if argv[1] == "-dirMp3toWAV": # convert mp3 to wav (batch)
if len(argv)==5:
path = argv[2]
if argv[3] not in ["8000", "16000", "32000", "44100"]:
print "Error. Unsupported sampling rate (must be: 8000, 16000, 32000 or 44100)."; return
if argv[4] not in ["1","2"]:
print "Error. Number of output channels must be 1 or 2"; return
if not os.path.isdir(path):
raise Exception("Input path not found!")
useMp3TagsAsNames = True
audioBasicIO.convertDirMP3ToWav(path, int(argv[3]), int(argv[4]), useMp3TagsAsNames)
else:
print "Error.\nSyntax: " + argv[0] + " -dirMp3toWAV <dirName> <sampling Freq> <numOfChannels>"
if argv[1] == "-dirWAVChangeFs": # convert mp3 to wav (batch)
if len(argv)==5:
path = argv[2]
if argv[3] not in ["8000", "16000", "32000", "44100"]:
print "Error. Unsupported sampling rate (must be: 8000, 16000, 32000 or 44100)."; return
if argv[4] not in ["1","2"]:
print "Error. Number of output channels must be 1 or 2"; return
if not os.path.isdir(path):
raise Exception("Input path not found!")
audioBasicIO.convertFsDirWavToWav(path, int(argv[3]), int(argv[4]))
else:
print "Error.\nSyntax: " + argv[0] + " -dirMp3toWAV <dirName> <sampling Freq> <numOfChannels>"
elif argv[1] == "-featureExtractionFile": # short-term and mid-term feature extraction to files (csv and numpy)
if len(argv)==7:
wavFileName = argv[2]
if not os.path.isfile(wavFileName):
raise Exception("Input audio file not found!")
if not (uT.isNum(argv[3]) and uT.isNum(argv[4]) and uT.isNum(argv[5]) and uT.isNum(argv[6])):
raise Exception("Mid-term and short-term window sizes and steps must be numbers!")
mtWin = float(argv[3])
mtStep = float(argv[4])
stWin = float(argv[5])
stStep = float(argv[6])
outFile = wavFileName
aF.mtFeatureExtractionToFile(wavFileName, mtWin, mtStep, stWin, stStep, outFile, True, True, True)
else:
print "Error.\nSyntax: " + argv[0] + " -featureExtractionFile <wavFileName> <mtWin> <mtStep> <stWin> <stStep>"
elif argv[1] == "-beatExtraction":
if len(argv)==4:
wavFileName = argv[2]
if not os.path.isfile(wavFileName):
raise Exception("Input audio file not found!")
if not (uT.isNum(argv[3])):
raise Exception("PLOT must be either 0 or 1")
if not ( (int(argv[3]) == 0) or (int(argv[3]) == 1) ):
raise Exception("PLOT must be either 0 or 1")
[Fs, x] = audioBasicIO.readAudioFile(wavFileName);
F = aF.stFeatureExtraction(x, Fs, 0.050*Fs, 0.050*Fs);
BPM, ratio = aF.beatExtraction(F, 0.050, int(argv[3])==1)
print "Beat: {0:d} bpm ".format(int(BPM))
print "Ratio: {0:.2f} ".format(ratio)
else:
print "Error.\nSyntax: " + argv[0] + " -beatExtraction <wavFileName> <PLOT (0 or 1)>"
elif argv[1] == '-featureExtractionDir': # same as -featureExtractionFile, in a batch mode (i.e. for each WAV file in the provided path)
if len(argv)==7:
path = argv[2]
if not os.path.isdir(path):
raise Exception("Input path not found!")
if not (uT.isNum(argv[3]) and uT.isNum(argv[4]) and uT.isNum(argv[5]) and uT.isNum(argv[6])):
raise Exception("Mid-term and short-term window sizes and steps must be numbers!")
mtWin = float(argv[3])
mtStep = float(argv[4])
stWin = float(argv[5])
stStep = float(argv[6])
aF.mtFeatureExtractionToFileDir(path, mtWin, mtStep, stWin, stStep, True, True, True)
else:
print "Error.\nSyntax: " + argv[0] + " -featureExtractionDir <path> <mtWin> <mtStep> <stWin> <stStep>"
elif argv[1] == '-featureVisualizationDir': # visualize the content relationships between recordings stored in a folder
if len(argv)==3:
if not os.path.isdir(argv[2]):
raise Exception("Input folder not found!")
aV.visualizeFeaturesFolder(argv[2], "pca", "")
elif argv[1] == '-fileSpectrogram': # show spectogram of a sound stored in a file
if len(argv)==3:
wavFileName = argv[2]
if not os.path.isfile(wavFileName):
raise Exception("Input audio file not found!")
[Fs, x] = audioBasicIO.readAudioFile(wavFileName)
x = audioBasicIO.stereo2mono(x)
specgram, TimeAxis, FreqAxis = aF.stSpectogram(x, Fs, round(Fs*0.040), round(Fs*0.040), True)
else:
print "Error.\nSyntax: " + argv[0] + " -fileSpectrogram <fileName>"
elif argv[1] == '-fileChromagram': # show spectogram of a sound stored in a file
if len(argv)==3:
wavFileName = argv[2]
if not os.path.isfile(wavFileName):
raise Exception("Input audio file not found!")
[Fs, x] = audioBasicIO.readAudioFile(wavFileName)
x = audioBasicIO.stereo2mono(x)
specgram, TimeAxis, FreqAxis = aF.stChromagram(x, Fs, round(Fs*0.040), round(Fs*0.040), True)
else:
print "Error.\nSyntax: " + argv[0] + " -fileSpectrogram <fileName>"
elif argv[1] == "-trainClassifier": # Segment classifier training (OK)
if len(argv)>6:
method = argv[2]
beatFeatures = (int(argv[3])==1)
listOfDirs = argv[4:len(argv)-1]
modelName = argv[-1]
aT.featureAndTrain(listOfDirs, 1, 1, aT.shortTermWindow, aT.shortTermStep, method.lower(), modelName, computeBEAT = beatFeatures)
else:
print "Error.\nSyntax: " + argv[0] + " -trainClassifier <method(svm or knn)> <beat features> <directory 1> <directory 2> ... <directory N> <modelName>"
elif argv[1] == "-trainRegression": # Segment regression model
if len(argv)==6:
method = argv[2]
beatFeatures = (int(argv[3])==1)
dirName = argv[4]
modelName = argv[5]
aT.featureAndTrainRegression(dirName, 1, 1, aT.shortTermWindow, aT.shortTermStep, method.lower(), modelName, computeBEAT = beatFeatures)
else:
print "Error.\nSyntax: " + argv[0] + " -trainRegression <method(svm or knn)> <beat features> <directory> <modelName>"
elif argv[1] == "-classifyFile": # Single File Classification (OK)
if len(argv)==5:
modelType = argv[2]
modelName = argv[3]
inputFile = argv[4]
if modelType not in ["svm", "knn"]:
raise Exception("ModelType has to be either svm or knn!")
if not os.path.isfile(modelName):
raise Exception("Input modelName not found!")
if not os.path.isfile(inputFile):
raise Exception("Input audio file not found!")
[Result, P, classNames] = aT.fileClassification(inputFile, modelName, modelType)
print "{0:s}\t{1:s}".format("Class","Probability")
for i,c in enumerate(classNames):
print "{0:s}\t{1:.2f}".format(c,P[i])
print "Winner class: " + classNames[int(Result)]
else:
print "Error.\nSyntax: " + argv[0] + " -classifyFile <method(svm or knn)> <modelName> <fileName>"
elif argv[1] == "-regressionFile": # Single File Classification (OK)
if len(argv)==5:
modelType = argv[2]
modelName = argv[3]
inputFile = argv[4]
if modelType not in ["svm", "knn"]:
raise Exception("ModelType has to be either svm or knn!")
if not os.path.isfile(inputFile):
raise Exception("Input audio file not found!")
R, regressionNames = aT.fileRegression(inputFile, modelName, modelType)
for i in range(len(R)):
print "{0:s}\t{1:.3f}".format(regressionNames[i], R[i])
#print "{0:s}\t{1:.2f}".format(c,P[i])
else:
print "Error.\nSyntax: " + argv[0] + " -regressionFile <method(svm or knn)> <modelName> <fileName>"
elif argv[1] == "-classifyFolder": # Directory classification (Ok)
if len(argv)==6 or len(argv)==5:
modelType = argv[2]
modelName = argv[3]
inputFolder = argv[4]
if len(argv)==6:
outputMode = argv[5]
else:
outputMode = "0"
if modelType not in ["svm", "knn"]:
raise Exception("ModelType has to be either svm or knn!")
if outputMode not in ["0","1"]:
raise Exception("outputMode has to be 0 or 1")
if not os.path.isfile(modelName):
raise Exception("Input modelName not found!")
files = '*.wav'
if os.path.isdir(inputFolder):
strFilePattern = os.path.join(inputFolder, files)
else:
strFilePattern = inputFolder + files
wavFilesList = []
wavFilesList.extend(glob.glob(strFilePattern))
wavFilesList = sorted(wavFilesList)
if len(wavFilesList)==0:
print "No WAV files found!"
return
Results = []
for wavFile in wavFilesList:
[Result, P, classNames] = aT.fileClassification(wavFile, modelName, modelType)
Result = int(Result)
Results.append(Result)
if outputMode=="1":
print "{0:s}\t{1:s}".format(wavFile,classNames[Result])
Results = numpy.array(Results)
# print distribution of classes:
[Histogram, _] = numpy.histogram(Results, bins=numpy.arange(len(classNames)+1))
for i,h in enumerate(Histogram):
print "{0:20s}\t\t{1:d}".format(classNames[i], h)
else:
print "Error.\nSyntax: " + argv[0] + " -classifyFolder <method(svm or knn)> <modelName> <folderName> <outputMode(0 or 1)"
elif argv[1] == "-regressionFolder": # Regression applied on the WAV files of a folder
if len(argv)==5:
modelType = argv[2]
modelName = argv[3]
inputFolder = argv[4]
if modelType not in ["svm", "knn"]:
raise Exception("ModelType has to be either svm or knn!")
files = '*.wav'
if os.path.isdir(inputFolder):
strFilePattern = os.path.join(inputFolder, files)
else:
strFilePattern = inputFolder + files
wavFilesList = []
wavFilesList.extend(glob.glob(strFilePattern))
wavFilesList = sorted(wavFilesList)
if len(wavFilesList)==0:
print "No WAV files found!"
return
Results = []
for wavFile in wavFilesList:
R, regressionNames = aT.fileRegression(wavFile, modelName, modelType)
Results.append(R)
Results = numpy.array(Results)
for i, r in enumerate(regressionNames):
[Histogram, bins] = numpy.histogram(Results[:, i])
centers = (bins[0:-1] + bins[1::]) / 2.0
plt.subplot(len(regressionNames), 1, i);
plt.plot(centers, Histogram)
plt.title(r)
plt.show()
# for h in Histogram:
# print "{0:20d}".format(h),
# if outputMode=="1":
# for i,h in enumerate(Histogram):
# print "{0:20s}\t\t{1:d}".format(classNames[i], h)
else:
print "Error.\nSyntax: " + argv[0] + " -regressionFolder <method(svm or knn)> <modelName> <folderName>"
elif argv[1] == '-trainHMMsegmenter_fromfile':
if len(argv)==7:
wavFile = argv[2]
gtFile = argv[3]
hmmModelName = argv[4]
if not uT.isNum(argv[5]):
print "Error: mid-term window size must be float!"; return
if not uT.isNum(argv[6]):
print "Error: mid-term window step must be float!"; return
mtWin = float(argv[5])
mtStep = float(argv[6])
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, mtWin, mtStep)
else:
print "Error.\nSyntax: " + argv[0] + " -trainHMMsegmenter_fromfile <wavFilePath> <gtSegmentFilePath> <hmmModelFileName> <mtWin> <mtStep>"
elif argv[1] == '-trainHMMsegmenter_fromdir':
if len(argv)==6:
dirPath = argv[2]
hmmModelName = argv[3]
if not uT.isNum(argv[4]):
print "Error: mid-term window size must be float!"
if not uT.isNum(argv[5]):
print "Error: mid-term window step must be float!"
mtWin = float(argv[4])
mtStep = float(argv[5])
aS.trainHMM_fromDir(dirPath, hmmModelName, mtWin, mtStep)
else:
print "Error.\nSyntax: " + argv[0] + " -trainHMMsegmenter_fromdir <dirPath> <hmmModelFileName> <mtWin> <mtStep>"
elif argv[1] == "-segmentClassifyFileHMM": # HMM-based segmentation-classification
if len(argv)==4:
hmmModelName = argv[2]
wavFile = argv[3]
gtFile = wavFile.replace('.wav', '.segments');
aS.hmmSegmentation(wavFile, hmmModelName, PLOT = True, gtFileName = gtFile)
else:
print "Error.\nSyntax: " + argv[0] + " -segmentClassifyHMM <hmmModelName> <fileName>"
elif argv[1] == '-segmentClassifyFile': # Segmentation-classification (fix-sized segment using knn or svm)
if (len(argv)==5):
modelType = argv[2]
modelName = argv[3]
inputWavFile = argv[4]
if modelType not in ["svm", "knn"]:
raise Exception("ModelType has to be either svm or knn!")
if not os.path.isfile(modelName):
raise Exception("Input modelName not found!")
if not os.path.isfile(inputWavFile):
raise Exception("Input audio file not found!")
gtFile = inputWavFile.replace('.wav', '.segments');
aS.mtFileClassification(inputWavFile, modelName, modelType, True, gtFile)
else:
print "Error.\nSyntax: " + argv[0] + " -segmentClassifyFile <method(svm or knn)> <modelName> <fileName>"
elif argv[1] == "-segmentationEvaluation":
if len(argv)==5:
methodName = argv[2]
modelName = argv[3]
dirName = argv[4]
aS.evaluateSegmentationClassificationDir(dirName, modelName, methodName)
else:
print "Error.\nSyntax: " + argv[0] + " -segmentationEvaluation <method(svm or knn)> <modelName> <directoryName>"
elif argv[1] == "-silenceRemoval":
if len(argv)==5:
inputFile = argv[2]
if not os.path.isfile(inputFile):
raise Exception("Input audio file not found!")
smoothingWindow = float(argv[3])
weight = float(argv[4])
[Fs, x] = audioBasicIO.readAudioFile(inputFile) # read audio signal
segmentLimits = aS.silenceRemoval(x, Fs, 0.05, 0.05, smoothingWindow, weight, False) # get onsets
for i, s in enumerate(segmentLimits):
strOut = "{0:s}_{1:.3f}-{2:.3f}.wav".format(inputFile[0:-4], s[0], s[1])
wavfile.write( strOut, Fs, x[int(Fs*s[0]):int(Fs*s[1])])
else:
print "Error.\nSyntax: " + argv[0] + " -silenceRemoval <inputFile> <smoothinWindow(secs)> <Threshold Weight>"
elif argv[1] == '-speakerDiarization': # speaker diarization (from file): TODO
inputFile = argv[2]
nSpeakers = int(argv[3])
useLDA = (int(argv[4])==1)
if useLDA:
aS.speakerDiarization(inputFile, nSpeakers, PLOT = True);
else:
aS.speakerDiarization(inputFile, nSpeakers, LDAdim = 0, PLOT = True);
#print speechLimits
elif argv[1] == "-speakerDiarizationScriptEval":
dir = argv[2]
listOfLDAs = [int(l) for l in argv[3::]]
aS.speakerDiarizationEvaluateScript(dir, listOfLDAs)
elif argv[1] == '-thumbnail': # music thumbnailing (OK)
if len(argv)==4:
inputFile = argv[2]
stWindow = 1.0
stStep = 1.0
if not os.path.isfile(inputFile):
raise Exception("Input audio file not found!")
[Fs, x] = audioBasicIO.readAudioFile(inputFile) # read file
if Fs == -1: # could not read file
return
try:
thumbnailSize = float(argv[3])
except ValueError:
print "Thumbnail size must be a float (in seconds)"
return
[A1, A2, B1, B2, Smatrix] = aS.musicThumbnailing(x, Fs, stWindow, stStep, thumbnailSize) # find thumbnail endpoints
# write thumbnails to WAV files:
thumbnailFileName1 = inputFile.replace(".wav","_thumb1.wav")
thumbnailFileName2 = inputFile.replace(".wav","_thumb2.wav")
wavfile.write(thumbnailFileName1, Fs, x[int(Fs*A1):int(Fs*A2)])
wavfile.write(thumbnailFileName2, Fs, x[int(Fs*B1):int(Fs*B2)])
print "1st thumbnail (stored in file {0:s}): {1:4.1f}sec -- {2:4.1f}sec".format(thumbnailFileName1, A1, A2)
print "2nd thumbnail (stored in file {0:s}): {1:4.1f}sec -- {2:4.1f}sec".format(thumbnailFileName2, B1, B2)
# Plot self-similarity matrix:
fig = plt.figure()
ax = fig.add_subplot(111, aspect='auto')
plt.imshow(Smatrix)
# Plot best-similarity diagonal:
Xcenter = (A1/stStep + A2/stStep) / 2.0
Ycenter = (B1/stStep + B2/stStep) / 2.0
e1 = matplotlib.patches.Ellipse((Ycenter, Xcenter), thumbnailSize * 1.4, 3,
angle=45, linewidth=3, fill=False)
ax.add_patch(e1)
plt.plot([B1, Smatrix.shape[0]], [A1, A1], color='k', linestyle='--', linewidth=2)
plt.plot([B2, Smatrix.shape[0]], [A2, A2], color='k', linestyle='--', linewidth=2)
plt.plot([B1, B1], [A1, Smatrix.shape[0]], color='k', linestyle='--', linewidth=2)
plt.plot([B2, B2], [A2, Smatrix.shape[0]], color='k', linestyle='--', linewidth=2)
plt.xlim([0, Smatrix.shape[0]])
plt.ylim([Smatrix.shape[1], 0])
ax.yaxis.set_label_position("right")
ax.yaxis.tick_right()
plt.xlabel('frame no')
plt.ylabel('frame no')
plt.title('Self-similarity matrix')
plt.show()
else:
print "Error.\nSyntax: " + argv[0] + " -thumbnail <filename> <thumbnailsize(seconds)>"
plt.subplot(2,2,1); plt.plot(F[0,:]); plt.xlabel('Frame no'); plt.ylabel('ZCR');
plt.subplot(2,2,2); plt.plot(F[1,:]); plt.xlabel('Frame no'); plt.ylabel('Energy');
plt.subplot(2,2,3); plt.plot(F[2,:]); plt.xlabel('Frame no'); plt.ylabel('Entropy of Energy');
plt.subplot(2,2,4); plt.plot(F[3,:]); plt.xlabel('Frame no'); plt.ylabel('Spectral Centroid');
plt.show()
#plt.savefig("data/graphs/" + song + ".png")
#plt.clf()
if model:
for i, genre in enumerate(genre_models):
print("--------------------")
print(genre)
print("--------")
for song in songs:
print("--------" + song + "-----------")
data = aT.fileClassification("data/mono/" + song, "data/" + genre,"svm")
print(song)
print(data)
# print "svmMusicGenre 1"
# Result, P, classNames = aT.fileClassification("data/Heavy.wav", "data/svmMusicGenre3","svm")
# print "Heavy.wav"
# print P
# print classNames
# Result, P, classNames = aT.fileClassification("data/Sakura.wav", "data/svmMusicGenre3","svm")
# print "Sakura.wav"
# print P
# print classNames
def main(argv):
if argv[1] == "-shortTerm":
for i in range(nExp):
[Fs, x] = audioBasicIO.readAudioFile("diarizationExample.wav")
duration = x.shape[0] / float(Fs)
t1 = time.clock()
F = audioFeatureExtraction.stFeatureExtraction(
x, Fs, 0.050 * Fs, 0.050 * Fs)
t2 = time.clock()
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.readAudioFile("diarizationExample.wav")
duration = x.shape[0] / float(Fs)
t1 = time.clock()
aT.fileClassification("diarizationExample.wav", "svmSM", "svm")
t2 = time.clock()
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.readAudioFile("diarizationExample.wav")
duration = x.shape[0] / float(Fs)
t1 = time.clock()
[flagsInd, classesAll, acc] = aS.mtFileClassification(
"diarizationExample.wav", "svmSM", "svm", False, '')
t2 = time.clock()
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.readAudioFile("diarizationExample.wav")
duration = x.shape[0] / float(Fs)
t1 = time.clock()
aS.hmmSegmentation('diarizationExample.wav',
'hmmRadioSM', False, '')
t2 = time.clock()
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.readAudioFile("diarizationExample.wav")
duration = x.shape[0] / float(Fs)
t1 = time.clock()
[Fs, x] = audioBasicIO.readAudioFile("diarizationExample.wav")
segments = aS.silenceRemoval(
x, Fs, 0.050, 0.050, smoothWindow=1.0, Weight=0.3, plot=False)
t2 = time.clock()
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.readAudioFile("scottish.wav")
duration1 = x1.shape[0] / float(Fs1)
t1 = time.clock()
[A1, A2, B1, B2, Smatrix] = aS.musicThumbnailing(
x1, Fs1, 1.0, 1.0, 15.0) # find thumbnail endpoints
t2 = time.clock()
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.readAudioFile("diarizationExample.wav")
duration1 = x1.shape[0] / float(Fs1)
t1 = time.clock()
aS.speakerDiarization("diarizationExample.wav",
4, LDAdim=0, PLOT=False)
t2 = time.clock()
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.readAudioFile("diarizationExample.wav")
duration1 = x1.shape[0] / float(Fs1)
t1 = time.clock()
aS.speakerDiarization("diarizationExample.wav", 4, PLOT=False)
t2 = time.clock()
perTime1 = duration1 / (t2 - t1)
print("Diarization \t {0:.1f} x realtime".format(perTime1))
# test on the testing set
# Copy and paste cross_validations and loop through the whole testing sets and get my own counts.
# All we need to record are the 4 types of conditions, in 4 lists with shared indexing
knn_true_positive = 0
svm_true_positive = 0
knn_false_negative = 0
svm_false_negative = 0
knn_true_negative = 0
svm_true_negative = 0
knn_false_positive = 0
svm_false_positive = 0
for test_file in glob.glob(testing_p_directory + '*.wav'):
# test svm
result_svm = aT.fileClassification(test_file, classifier_location + 'svm', 'svm')
# test knn
result_knn = aT.fileClassification(test_file, classifier_location + 'knn', 'knn')
# update counts
if result_knn[2][result_knn[0]] == 'p':
knn_true_positive += 1
else:
knn_false_negative += 1
if result_svm[2][int(result_svm[0])] == 'p':
svm_true_positive += 1
else:
svm_false_negative += 1
for test_file in glob.glob(testing_np_directory + '*.wav'):
# test svm