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SpeechNet.py
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SpeechNet.py
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from keras.models import Sequential
from keras.layers.core import Dense, Activation,Dropout ,TimeDistributedDense,Reshape,RepeatVector,TimeDistributedMerge
from keras.layers.recurrent import LSTM,GRU
from keras.layers import Embedding
from random import random
import numpy as np
import pandas as pd
import datetime
import analyse as an
import glob as gb
import scipy.io.wavfile as sc
import hashlib
import SpeechPitchExtraction as lp
from keras import backend as K
import random
class speechLSTM:
# Initializing the LSTM Model
def __init__(self):
self.prevData = 30
self.batchsize=200
self.model = Sequential()
def build_nnet(self):
self.model.add(LSTM(300,return_sequences=True, stateful=True,
batch_input_shape=(self.batchsize, self.prevData, 2)))
self.model.add(Activation("linear"))
self.model.add(Dropout(0.5))
# self.model.add(LSTM(400,return_sequences=True,stateful=True))
# self.model.add(Activation("linear"))
# self.model.add(Dropout(0.5))
# self.model.add(LSTM(500, return_sequences=True, stateful=True))
# self.model.add(Activation("linear"))
# self.model.add(Dropout(0.5))
self.model.add(LSTM(400, return_sequences=True, stateful=True))
self.model.add(Activation("relu"))
# self.model.add(Dropout(0.5))
# self.model.add(LSTM(700, return_sequences=True, stateful=True))
# self.model.add(Activation("linear"))
self.model.add(LSTM(500, return_sequences=False, stateful=True))
self.model.add(Activation("linear"))
self.model.add(Dropout(0.5))
self.model.add(Dense(1, activation='sigmoid'))
self.model.compile(loss='binary_crossentropy', optimizer='adadelta')
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 get_train_test_data(self,data,percent_split):
ftestList,ltestlist,fvalidList,lvalidList,ftrainList,ltrainList=[],[],[],[],[],[]
noOfTrainSamples = len(data)*(1-percent_split)
noOfTestSamples = len(data)-noOfTrainSamples
self.batchsize = int(noOfTestSamples)
noOfTrainSamples = int((noOfTrainSamples - self.prevData)/noOfTestSamples)
for i in range(int(noOfTrainSamples)*self.batchsize):
#ltrainList.append(data.iloc[i:i+self.prevData, 2:].as_matrix())
ftrainList.append(data.iloc[i:i+self.prevData, 0:2].as_matrix())
ltrainList = data.iloc[0:int(noOfTrainSamples)*self.batchsize, 2:].values
for i in range(self.batchsize):
fvalidList.append(data.iloc[i:i + self.prevData, 0:2].as_matrix())
lvalidList = data.iloc[0:self.batchsize, 2:].values
randNum = random.randint(0,noOfTrainSamples)
for i in range(randNum,randNum+self.batchsize):
ftestList.append(data.iloc[i:i + self.prevData, 0:2].as_matrix())
ltestlist = data.iloc[randNum: randNum+self.batchsize, 2:].values
return np.array(ftestList),np.array(ltestlist),np.array(ftrainList),np.array(ltrainList),np.array(fvalidList),np.array(lvalidList)
def trainNNet(self,data_,label_,valid_data,valid_label):
data = data_/data_.max(axis=0)
label = label_/label_.max(axis=0)
valid_data = valid_data/valid_data.max(axis=0)
valid_label = valid_label/valid_label.max(axis=0)
self.model.fit(data, label, batch_size=self.batchsize, nb_epoch=5,validation_data=(valid_data,valid_label),show_accuracy=True,shuffle=False)
def predict(self,ftest_,ltest_):
ltest=ltest_/ltest_.max(axis=0)
ftest=ftest_/ftest_.max(axis=0)
count=0
predcited_data= self.model.predict_on_batch(ftest)
print ("Score:",self.model.evaluate(ftest,ltest, show_accuracy=True))
for i in range(len(predcited_data)):
if predcited_data[0][i]==ltest[0][i]:
count+=1
print ("No of element Matching:",count)
print ("No of dissimilar elements:",len(predcited_data[0])-count)
print predcited_data
print ltest
def saveModel(self):
self.model.save_weights("/media/vyassu/OS/Users/vyas/Documents/Assigments/BigData/", overwrite=False)
def getIntermediateLayer(self):
get_3rd_layer_output = K.function([self.model.layers[0].input],
[self.model.layers[3].get_output(train=False)])
#layer_output = get_3rd_layer_output[0]
print K.get_value(get_3rd_layer_output)
def main():
print ("Model Creation Start time:",datetime.datetime.now().time())
testLSTM = speechLSTM()
print ("Model Creation End time:",datetime.datetime.now().time())
#dataframe = pd.DataFrame(testLSTM.load_data_file())
#dataframe.to_csv("/media/vyassu/OS/Users/vyas/Documents/Assigments/BigData/Test-TrainingData.csv")
dataframe = pd.read_csv("/media/vyassu/OS/Users/vyas/Documents/Assigments/BigData/Test-TrainingData.csv",usecols=['0','1','2'])
print ("File Data load End time:",datetime.datetime.now().time())
ftest, ltest, ftrain, ltrain,fvalid,lvalid = testLSTM.get_train_test_data(dataframe,0.2)
print ("Test and Train data created:",datetime.datetime.now().time())
print ("LSTM Model creation started:", datetime.datetime.now().time())
testLSTM.build_nnet()
print ("LSTM Model creation ended:", datetime.datetime.now().time())
print ("LSTM Model Training started:", datetime.datetime.now().time())
testLSTM.trainNNet(ftrain,ltrain,fvalid,lvalid)
print ("LSTM Model Training ended:", datetime.datetime.now().time())
testLSTM.predict(ftest,ltest)
#testLSTM.getIntermediateLayer()
if __name__ == '__main__':
main()