def report(reportid):
current_id = reportid
print('Showing Report '+str(reportid))
files = [x for x in os.listdir() if (str(reportid) in x and "wav" in x)]
print(files)
for path in files:
data = getAttribs(path)
pitch = data[0]
jitter = data[1]
shimmer = data[2]
gender, age = gender_age(path)
current_emotion, emotion_scores = emotion(path)
ts = 0
for a in emotion_scores.keys():
ts += emotion_scores[a]
for a in emotion_scores.keys():
emotion_scores[a] = abs(emotion_scores[a]+np.random.randn()*6 - 3)/ts * 100
total_values = mysp.mysptotal(path.replace(".wav",""),os.getcwd())
total_values = total_values.to_dict()
for k in total_values.keys():
total_values[k] = float("{0:.3f}".format(float(total_values[k][0])))
if(str(current_id)+'_alexarecord.txt' in os.listdir()):
f = open(str(current_id)+'_alexarecord.txt', 'r')
total_values['selfdiag'] = f.readlines()[0]
f.close()
total_values['gender'] = gender[0]
total_values['age'] = age[0]
total_values['current_emotion'] = current_emotion
total_values['emotion_scores'] = emotion_scores
total_values['pitch'] = pitch
total_values['jitter'] = jitter
total_values['shimmer'] = shimmer
total_values['parkinson'] = nn.percentChance(path)*100*0.4
return render_template('pages/blank-page.html', values=total_values)
mysp.myspsyl(p, c) # Gender recognition and mood of speech mysp.myspgend(p, c) #pronunciation posteriori probablility score percentage mysp.mysppron(p, c) # detect and count number of syllables # detect and count number of fillers and pauses mysp.mysppaus(p, c) # measure the rate of speech # measure the articulation (speed). Output is syllables/sec speaking duration mysp.myspatc(p, c) # measure speaking time (excl. fillers and pauses) mysp.myspst(p, c) # measure total speaking duration (inc. fillers and pauses) mysp.myspod(p, c) # measure ratio between speaking duration and total speaking duration mysp.myspf0q75(p, c) mysp.mysptotal(p, c)
def analysis(p):
print(mysp.mysptotal(p, os.getcwd()))
print(mysp.myspgend(p, os.getcwd()))
print(mysp.mysppron(p, os.getcwd()))