from audioSegmentation import speakerDiarization
if __name__=='__main__':
print speakerDiarization('./data/test.wav',0)
def speakerDiarizationWrapper(inputFile, numSpeakers, useLDA): if useLDA: aS.speakerDiarization(inputFile, numSpeakers, PLOT = True); else: aS.speakerDiarization(inputFile, numSpeakers, LDAdim = 0, PLOT = True);
mylog.info('Segmenting: %s',inputFile)
# not sure how this code handles multiple channels in the inputFile
numOfSpeakers = 0 # set to zero for unknown - algorithm decides
# set to number if known
# use the indices to write out a text file containing the timing information
# for segmentation
dirname,basename = os.path.split(inputFile)
basename,ext = os.path.splitext(basename)
outFile = os.path.sep.join([dirname,basename + '-seg.txt'])
try:
segInfo = aS.speakerDiarization(inputFile,numOfSpeakers)
except:
# if the segmentation fails, log it and continue
# the code will fail if there is only one speaker in the file
# i.e. this file ./Initial Audio Files/Dayton Fire Department/Haz-Mat Incident/14-23-28-clean.wav
mylog.warning('Segmentation failed: %s', inputFile)
with open(outFile,'w') as f:
# write a single line to the file, -1 as duration indicates entire file
dumbWriteFunc(f,0,0,0.0,-1.0)
continue
# segInfo contains a list
# segInfo[0] is the time step of the analysis
# segInfo[1] is np array of the speaker ID at each time step
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)>"
if filename not in processlist:
print('processing ' + filename)
processlist.append(listdir[i])
filesize = os.path.getsize(filename)
if filesize > int(500):
#if over 20 minute of audio collected (10.580MB), assume 2 speakers
shutil.copy(
incoming_dir + filename,
hostdir + '/pyaudioanalysis3/data/' + filename)
g = aS.speakerDiarization(filename,
2,
mtSize=2.0,
mtStep=0.2,
stWin=0.05,
LDAdim=35,
PLOT=False)
s0seg = list()
s1seg = list()
allseg = list()
for i in range(len(g) - 1):
if i == 0:
start = i / 5.0
else:
if g[i] == g[i + 1]:
pass
#continue where left off to find start length, 20 milliseconds
def speakerDiarizationWrapper(inputFile, numSpeakers, useLDA):
if useLDA:
aS.speakerDiarization(inputFile, numSpeakers, PLOT=True)
else:
aS.speakerDiarization(inputFile, numSpeakers, LDAdim=0, PLOT=True)
def speakerDiarizationWrapper(inputFile, numSpeakers, useLDA):
if useLDA:
aS.speakerDiarization(inputFile, numSpeakers, plot_res=True)
else:
aS.speakerDiarization(inputFile, numSpeakers, lda_dim=0, plot_res=True)
from audioSegmentation import speakerDiarization
if __name__ == '__main__':
print speakerDiarization('./data/test.wav', 0)
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))
durations = []
runtimes = []
diarizations = []
for file in os.listdir("AudioTest"):
if file.endswith(".wav"):
audio_path = "AudioTest/" + file
#audio_path="Audio/cpb-aacip-106-31cjt1bv.wav"
with contextlib.closing(wave.open(audio_path, 'r')) as f:
frames = f.getnframes()
rate = f.getframerate()
duration = frames / float(rate)
durations.append(duration)
num_speakers = 0
t0 = time.clock()
speaker_array = aS.speakerDiarization(audio_path,
num_speakers,
PLOT=False)
t1 = time.clock() - t0
runtimes.append(t1)
current = 0
count = 0
total = 0
startResult = defaultdict(list)
endResult = defaultdict(list)
for i in speaker_array:
total = total + 1
if i == current:
count += 1
else:
start = datetime.timedelta(seconds=(total - count) * .2)
end = datetime.timedelta(seconds=total * .2)
#from pyAudioAnalysis import audioSegmentation as aS
#from pyAudioAnalysis import audioBasicIO as aIO
import audioSegmentation as aS
#[Fs, x] = aIO.readAudioFile("data/Open_Mic.wav")
#segments = aS.silenceRemoval(x, Fs, 0.020, 0.020, smoothWindow = 1.0, weight = 0.3, plot = True)
#print(segments)
#segments = aS.silenceRemoval("data/Open_Mic.wav",16000,0.020, 0.020,smoothWindow=0.5, weight=0.5, plot=True )
#print(segments)
#python audioAnalysis.py speakerDiarization -i data/diarizationExample.wav --num 0
#from pyAudioAnalysis import audioSegmentation as aS
#[flagsInd, classesAll, acc, CM] = aS.mtFileClassification("data/scottish.wav", "data/svmSM", "svm", True, 'data/scottish.segments')
#aS.speakerDiarization(filename="data/diarizationExample.wav", n_speakers=4, plot_res = True)
aS.speakerDiarization(filename="data/Open_Mic.wav",
n_speakers=6,
plot_res=True)