Added app folder

stress-detection/app/model.joblib

+
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stress-detection/app/stress_detection.py

+import os
+import random
+import sys
+
+import glob 
+import keras
+import IPython.display as ipd
+import librosa
+import librosa.display
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import plotly.graph_objs as go
+import plotly.offline as py
+import plotly.tools as tls
+import seaborn as sns
+import scipy.io.wavfile
+import tensorflow
+py.init_notebook_mode(connected=True)
+
+from tensorflow.keras import regularizers
+from tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler, EarlyStopping
+from tensorflow.keras.callbacks import  History, ReduceLROnPlateau, CSVLogger
+from tensorflow.keras.models import Model, Sequential
+from tensorflow.keras.layers import Dense, Embedding, LSTM
+from tensorflow.keras.layers import Input, Flatten, Dropout, Activation, BatchNormalization
+from tensorflow.keras.layers import Conv1D, MaxPooling1D, AveragePooling1D
+from tensorflow.keras.preprocessing import sequence
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from tensorflow.keras.preprocessing.text import Tokenizer
+from keras.utils import np_utils
+from tensorflow.keras.utils import to_categorical
+
+from sklearn.metrics import confusion_matrix
+from sklearn.preprocessing import LabelEncoder
+
+from scipy.fftpack import fft
+from scipy import signal
+from scipy.io import wavfile
+from tqdm import tqdm
+
+input_duration=3
+
+data_path=r'Audio_Speech_Actors_01-24'
+dir_list=os.listdir(data_path)
+print(dir_list)
+
+file_paths=[]
+emotions=[]
+
+for actor in dir_list:
+    #print(actor)
+    actor_path=os.path.join(data_path,actor)
+    #print(actor_path)
+    file_names=os.listdir(actor_path)
+    #print(file_names)
+    for file_name in file_names:
+        file_path=os.path.join(actor_path,file_name)
+        #print(file_path)
+        nm = file_name[:-4].split('-')
+        emotion=int(nm[2])
+        gender=int(nm[-1])%2
+        actor = int(nm[-1])
+        if(actor!=21 and actor!=22 and actor!=23 and actor!=24):
+            file_paths.append(file_path)
+            emotions.append(emotion)
+            print(gender,actor)
+
+print(file_paths[100],emotions[100])
+
+def noise(data):
+    """
+    Adding White Noise.
+    """
+    # you can take any distribution from https://docs.scipy.org/doc/numpy-1.13.0/reference/routines.random.html
+    noise_amp = 0.005*np.random.uniform()*np.amax(data)
+    data = data.astype('float64') + noise_amp * np.random.normal(size=data.shape[0])
+    return data
+
+def pitch(data, sample_rate):
+    """
+    Pitch Tuning.
+    """
+    bins_per_octave = 12
+    pitch_pm = 2
+    pitch_change =  pitch_pm * 2*(np.random.uniform())   
+    data = librosa.effects.pitch_shift(data.astype('float64'), 
+                                      sample_rate, n_steps=pitch_change, 
+                                      bins_per_octave=bins_per_octave)
+    return data
+
+def log_specgram(audio, sample_rate, window_size=20,
+                 step_size=10, eps=1e-10):
+    nperseg = int(round(window_size * sample_rate / 1e3))
+    noverlap = int(round(step_size * sample_rate / 1e3))
+    freqs, times, spec = signal.spectrogram(audio,
+                                    fs=sample_rate,
+                                    window='hann',
+                                    nperseg=nperseg,
+                                    noverlap=noverlap,
+                                    detrend=False)
+    return freqs, times, np.log(spec.T.astype(np.float32) + eps)
+
+
+data1=[]
+
+for i in tqdm(range(len(file_paths))):
+    
+    #print(i,file_paths[i])
+    X, sample_rate = librosa.load(file_paths[i], res_type='kaiser_fast',duration=input_duration,sr=22050*2,offset=0.5)
+    
+    sample_rate = np.array(sample_rate)
+    mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=13), axis=0)
+    feature = mfccs
+    data1.append(feature)
+
+    data2=[]
+
+for i in tqdm(range(len(file_paths))):
+    X, sample_rate = librosa.load(file_paths[i], res_type='kaiser_fast',duration=input_duration,sr=22050*2,offset=0.5)
+    X = noise(X)
+    sample_rate = np.array(sample_rate)
+    mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=13), axis=0)
+    feature = mfccs
+    a = random.uniform(0, 1)
+    data2.append(feature)
+
+    data3=[]
+for i in tqdm(range(len(file_paths))):
+    X, sample_rate = librosa.load(file_paths[i], res_type='kaiser_fast',duration=input_duration,sr=22050*2,offset=0.5)
+    X = pitch(X, sample_rate)
+    sample_rate = np.array(sample_rate)
+    mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=13), axis=0)
+    feature = mfccs
+    a = random.uniform(0, 1)
+    data3.append(feature)
+
+
+data1_new=pd.DataFrame(data1)
+data2_new=pd.DataFrame(data2)
+data3_new=pd.DataFrame(data3)
+
+data1_new=data1_new.fillna(0)
+data2_new=data2_new.fillna(0)
+data3_new=data3_new.fillna(0)
+
+target=[]
+
+for emotion in emotions:
+    
+    if(emotion==1 or emotion==2 or emotion==3 or emotion==8):
+        target.append(0)
+    elif(emotion==4 or emotion==5 or emotion==6 or emotion==7):
+        target.append(1)
+
+data_new=pd.concat([data1_new,data2_new,data3_new]).values
+#data_new=data1_new.values
+
+target_new=np.array(target)
+target_new=np.concatenate([target,target,target])
+target_new=np_utils.to_categorical(target_new)
+
+print(data_new.shape)
+
+from sklearn.preprocessing import MinMaxScaler
+import joblib
+
+scaler=MinMaxScaler()
+data_new=scaler.fit_transform(data_new)
+data_new=data_new.reshape(data_new.shape[0],data_new.shape[1],1)
+
+joblib.dump(scaler,'scaler.sav')
+
+from sklearn.model_selection import train_test_split
+
+train_data,test_data,train_target,test_target=train_test_split(data_new,target_new,test_size=0.2)
+
+
+# Set up Keras util functions
+
+from keras import backend as K
+
+def precision(y_true, y_pred):
+    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
+    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
+    precision = true_positives / (predicted_positives + K.epsilon())
+    return precision
+
+
+def recall(y_true, y_pred):
+    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
+    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
+    recall = true_positives / (possible_positives + K.epsilon())
+    return recall
+
+
+def fscore(y_true, y_pred):
+    if K.sum(K.round(K.clip(y_true, 0, 1))) == 0:
+        return 0
+
+    p = precision(y_true, y_pred)
+    r = recall(y_true, y_pred)
+    f_score = 2 * (p * r) / (p + r + K.epsilon())
+    return f_score
+
+def get_lr_metric(optimizer):
+    def lr(y_true, y_pred):
+        return optimizer.lr
+    return lr
+
+
+# New model
+model = Sequential()
+model.add(Conv1D(256, 8, padding='valid',input_shape=(train_data.shape[1],1)))
+model.add(Activation('relu'))
+model.add(Conv1D(256, 8, padding='same'))
+model.add(BatchNormalization())
+model.add(Activation('relu'))
+model.add(Dropout(0.25))
+model.add(MaxPooling1D(pool_size=(8)))
+model.add(Conv1D(128, 8, padding='same'))
+model.add(Activation('relu'))
+model.add(Conv1D(128, 8, padding='same'))
+model.add(Activation('relu'))
+model.add(Conv1D(128, 8, padding='same'))
+model.add(Activation('relu'))
+model.add(Conv1D(128, 8, padding='same'))
+model.add(BatchNormalization())
+model.add(Activation('relu'))
+model.add(Dropout(0.25))
+model.add(MaxPooling1D(pool_size=(8)))
+model.add(Conv1D(64, 8, padding='same'))
+model.add(Activation('relu'))
+model.add(Conv1D(64, 8, padding='same'))
+model.add(Activation('relu'))
+model.add(Flatten())
+# Edit according to target class no.
+model.add(Dense(2))
+model.add(Activation('softmax'))
+model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
+model.summary()
+
+# Model Training
+
+lr_reduce = ReduceLROnPlateau(monitor='val_loss', factor=0.9, patience=20, min_lr=0.000001)
+# Please change the model name accordingly.
+mcp_save = ModelCheckpoint('C:\\Users\\acer\\OneDrive\\3.1 Stress detector Model\\Data_noiseNshift.h5', save_best_only=True, monitor='val_loss', mode='min')
+cnnhistory=model.fit(train_data, train_target, batch_size=16, epochs=100,
+                     validation_data=(test_data, test_target), callbacks=[mcp_save, lr_reduce])
+
+plt.plot(cnnhistory.history['loss'])
+plt.plot(cnnhistory.history['val_loss'])
+plt.title('model loss')
+plt.ylabel('loss')
+plt.xlabel('epoch')
+plt.legend(['train', 'test'], loc='upper left')
+plt.show()
+
+# Saving the model.json
+import json
+model_json = model.to_json()
+with open("C:\\Users\\acer\\OneDrive\\3.1 Stress detector Model\\model.json", "w") as json_file:
+    json_file.write(model_json)
+
+import tensorflow as tf
+from tensorflow.keras.initializers import glorot_uniform
+loaded_model = tf.keras.models.load_model("C:\\Users\\acer\\OneDrive\\3.1 Stress detector Model\\Data_noiseNshift.h5",custom_objects={'GlorotUniform': glorot_uniform()})
+print("Loaded model from disk")
+ 
+# evaluate loaded model on test data
+loaded_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
+score = loaded_model.evaluate(test_data, test_target, verbose=0)
+print("%s: %.4f%%" % (loaded_model.metrics_names[1], score[1]*100))
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