def load_data_file(self):
outputdata = []
for f in gb.glob("/media/vyassu/OS/Users/vyas/Documents/Assigments/BigData/AudioData/DC/*.wav"):
frate, inputdata = sc.read(f)
pitch=lp.getPitch(f)
emotion = ""
loudness = abs(an.loudness(inputdata))
filename = f.split("/")[-1].split(".")[0]
if filename[0] == "s":
emotion = filename[0:2]
##emotion = float(int(hashlib.md5(emotion).hexdigest(), 16))
else:
emotion = filename[0]
##emotion = float(int(hashlib.md5(emotion).hexdigest(), 16))
outputdata.append(list([loudness,pitch, emotion]))
for f in gb.glob("/media/vyassu/OS/Users/vyas/Documents/Assigments/BigData/AudioData/JE/*.wav"):
frate, inputdata = sc.read(f)
pitch = lp.getPitch(f)
emotion = ""
loudness = abs(an.loudness(inputdata))
filename = f.split("/")[-1].split(".")[0]
if filename[0] == "s":
emotion = filename[0:2]
##emotion = float(int(hashlib.md5(emotion).hexdigest(), 16))
else:
emotion = filename[0]
##emotion = float(int(hashlib.md5(emotion).hexdigest(), 16))
outputdata.append(list([loudness, pitch, emotion]))
for f in gb.glob("/media/vyassu/OS/Users/vyas/Documents/Assigments/BigData/AudioData/JK/*.wav"):
frate, inputdata = sc.read(f)
pitch = lp.getPitch(f)
emotion = ""
loudness = abs(an.loudness(inputdata))
filename = f.split("/")[-1].split(".")[0]
if filename[0] == "s":
emotion = filename[0:2]
##emotion = float(int(hashlib.md5(emotion).hexdigest(), 16))
else:
emotion = filename[0]
##emotion = float(int(hashlib.md5(emotion).hexdigest(), 16))
outputdata.append(list([loudness, pitch, emotion]))
for f in gb.glob("/media/vyassu/OS/Users/vyas/Documents/Assigments/BigData/AudioData/KL/*.wav"):
frate, inputdata = sc.read(f)
pitch = lp.getPitch(f)
emotion = ""
loudness = abs(an.loudness(inputdata))
filename = f.split("/")[-1].split(".")[0]
if filename[0] == "s":
emotion = filename[0:2]
##emotion = float(int(hashlib.md5(emotion).hexdigest(), 16))
else:
emotion = filename[0]
##emotion = float(int(hashlib.md5(emotion).hexdigest(), 16))
outputdata.append(list([loudness, pitch, emotion]))
return outputdata
def dataconverter(filename):
frate,inputdata = sc.read(filename=filename)
pitch = lp.getPitch(filename, frate)
emotion = ""
loudness = abs(an.loudness(inputdata))
filename = filename.split("/")[-1].split(".")[0]
if filename[0] == "s":
emotion = filename[0:2]
emotion = ord(emotion[0])+ord(emotion[1])
else:
emotion = filename[0]
emotion = float(ord(emotion))/100
return [float(loudness), float(pitch), emotion]
def dataconverter(filename):
frate, inputdata = sc.read(filename=filename)
pitch = lp.getPitch(filename, frate)
emotion = ""
loudness = abs(an.loudness(inputdata))
filename = filename.split("/")[-1].split(".")[0]
if filename[0] == "s":
emotion = filename[0:2]
emotion = ord(emotion[0]) + ord(emotion[1])
else:
emotion = filename[0]
emotion = float(ord(emotion)) / 100
return [float(loudness), float(pitch), emotion]
def load_data(self, filename):
outputdata = []
# Loop to traverse through the input data file path
for f in gb.glob(filename):
frate, inputdata = sc.read(f)
pitch = lp.getPitch(f, frate)
loudness = abs(an.loudness(inputdata))
filename = f.split("/")[-1].split(".")[0]
if filename[0] == "s":
emotion = filename[0:2]
else:
emotion = filename[0]
outputdata.append(list([loudness, pitch, emotion]))
return outputdata
def load_data(self,filename):
outputdata=[]
# Loop to traverse through the input data file path
for f in gb.glob(filename):
frate, inputdata = sc.read(f)
pitch = lp.getPitch(f,frate)
loudness = abs(an.loudness(inputdata))
filename = f.split("/")[-1].split(".")[0]
if filename[0] == "s":
emotion = filename[0:2]
else:
emotion = filename[0]
outputdata.append(list([loudness, pitch, emotion]))
return outputdata
def load_data(self):
datadirectory = self.working_directory+"Data/"
outputdata=[]
for f in gb.glob(datadirectory+"*.wav"):
frate, inputdata = sc.read(f)
pitch = lp.getPitch(f,frate)
emotion = ""
loudness = abs(an.loudness(inputdata))
filename = f.split("/")[-1].split(".")[0]
if filename[0] == "s":
emotion = filename[0:2]
else:
emotion = filename[0]
outputdata.append(list([loudness, pitch, emotion]))
return outputdata
def load_data_file(self):
outputdata = []
for f in gb.glob(
"/media/vyassu/OS/Users/vyas/Documents/Assigments/BigData/AudioData/KL/*.wav"
):
frate, inputdata = sc.read(f)
pitch = lp.getPitch(f, frate)
emotion = ""
loudness = abs(an.loudness(inputdata))
filename = f.split("/")[-1].split(".")[0]
if filename[0] == "s":
emotion = filename[0:2]
emotion = float(int(hashlib.md5(emotion).hexdigest(), 16))
else:
emotion = filename[0]
emotion = float(int(hashlib.md5(emotion).hexdigest(), 16))
outputdata.append(list([loudness, pitch, emotion]))
return outputdata
def load_data_file(self, audiodatapath):
outputdata = [] # Variable to store the speech features and emotions
# Looping all the wave files present in the path
for f in gb.glob(audiodatapath):
frate, inputdata = sc.read(f)
# Extracting the pitch from the wav file using Aubio speech API
pitch = lp.getPitch(f, frate)
# Extracting loudness of the voice from the Wave file
loudness = abs(an.loudness(inputdata))
# Extracting the emotion type from the wave file only for training stage
filename = f.split("/")[-1].split(".")[0]
# Condition to differentiate the various types of emotions
if filename[0] == "s":
emotion = filename[0:2]
else:
emotion = filename[0]
# Creating the dataset consisting of list of features and corresponding emotion type
outputdata.append(list([loudness, pitch, emotion]))
return outputdata
def load_data_file(self):
outputdata = [] # Variable to store the speech features and emotions
# Looping all the wave files present in the path
for f in gb.glob(self.working_directory+"AudioData/*/*.wav"):
frate, inputdata = sc.read(f)
# Extracting the pitch from the wav file using Aubio speech API
pitch=lp.getPitch(f,frate)
# Extracting loudness of the voice from the Wave file
loudness = abs(an.loudness(inputdata))
# Extracting the emotion type from the wave file only for training stage
filename = f.split("/")[-1].split(".")[0]
# Condition to differentiate the various types of emotions
if filename[0] == "s":
emotion = filename[0:2]
else:
emotion = filename[0]
# Creating the dataset consisting of list of features and corresponding emotion type
outputdata.append(list([loudness,pitch, emotion]))
return outputdata
