Upload ser_detection.py

ser_detection.py

@@ -0,0 +1,149 @@
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+from flask import Flask, make_response, render_template, request, jsonify, redirect, url_for, send_from_directory
+from flask_cors import CORS
+
+import sys
+import os
+
+import librosa
+import librosa.display
+import numpy as np
+
+import warnings
+import tensorflow as tf
+from keras.models import Sequential
+from keras.layers import Dense
+from keras.utils import to_categorical
+from keras.layers import Flatten, Dropout, Activation
+from keras.layers import Conv2D, MaxPooling2D
+from keras.layers.normalization import BatchNormalization
+from sklearn.model_selection import train_test_split
+from tqdm import tqdm
+# import scipy.io.wavfile as wav
+# from speechpy.feature import mfcc
+
+import pyaudio
+import wave
+
+warnings.filterwarnings("ignore")
+
+app = Flask(__name__)
+CORS(app)
+
+classLabels = ('Angry', 'Fear', 'Disgust', 'Happy', 'Sad', 'Surprised', 'Neutral')
+numLabels = len(classLabels)
+in_shape = (39,216)
+model = Sequential()
+
+model.add(Conv2D(8, (13, 13), input_shape=(in_shape[0], in_shape[1], 1)))
+model.add(BatchNormalization(axis=-1))
+model.add(Activation('relu'))
+model.add(Conv2D(8, (13, 13)))
+model.add(BatchNormalization(axis=-1))
+model.add(Activation('relu'))
+model.add(MaxPooling2D(pool_size=(2, 1)))
+model.add(Conv2D(8, (3, 3)))
+model.add(BatchNormalization(axis=-1))
+model.add(Activation('relu'))
+model.add(Conv2D(8, (1, 1)))
+model.add(BatchNormalization(axis=-1))
+model.add(Activation('relu'))
+model.add(MaxPooling2D(pool_size=(2, 1)))
+model.add(Flatten())
+model.add(Dense(64))
+model.add(BatchNormalization())
+model.add(Activation('relu'))
+model.add(Dropout(0.2))
+
+model.add(Dense(numLabels, activation='softmax'))
+model.compile(loss='binary_crossentropy', optimizer='adam',
+                           metrics=['accuracy'])
+# print(model.summary(), file=sys.stderr)
+
+model.load_weights('speech_emotion_detection_ravdess_savee.h5')
+
+def detect_emotion(file_name):
+    X, sample_rate = librosa.load(file_name, res_type='kaiser_best',duration=2.5,sr=22050*2,offset=0.5)
+    sample_rate = np.array(sample_rate)
+    mfccs = librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=39)
+    feature = mfccs
+    print("Feature_shape =>",feature.shape)
+    feature = feature.reshape(39, 216, 1)
+    result = classLabels[np.argmax(model.predict(np.array([feature])))]
+    print("Result ==> ",result) 
+    return result
+
+@app.route("/speech-emotion-recognition/")
+def emotion_detection():
+    filename = 'audio_files/Happy.wav'
+
+    result = detect_emotion(filename)
+    return result
+
+@app.route("/record_audio/")
+def record_audio():
+    CHUNK = 1024 
+    FORMAT = pyaudio.paInt16 #paInt8
+    CHANNELS = 2 
+    RATE = 44100 #sample rate
+    RECORD_SECONDS = 4
+    
+    fileList = os.listdir('recorded_audio') 
+    print("Audio File List ==> ",fileList)
+
+    new_wav_file = ""
+    
+    if(fileList):
+        filename_list = []
+        for i in fileList:
+            print(i)
+            filename = i.split('.')[0]
+            filename_list.append(filename)
+        
+        max_file = max(filename_list)
+        print(type(max_file))
+
+        new_wav_file = int(max_file) + 1
+    else:
+        new_wav_file="1"
+
+    new_wav_file = str(new_wav_file) + ".wav"
+    filepath = os.path.join('recorded_audio', new_wav_file)
+    WAVE_OUTPUT_FILENAME = filepath
+
+    print(WAVE_OUTPUT_FILENAME)
+
+    p = pyaudio.PyAudio()
+
+    stream = p.open(format=FORMAT,
+                    channels=CHANNELS,
+                    rate=RATE,
+                    input=True,
+                    frames_per_buffer=CHUNK) #buffer
+
+    print("* recording")
+
+    frames = []
+
+    for i in range(0, int(RATE / CHUNK * RECORD_SECONDS)):
+        data = stream.read(CHUNK)
+        frames.append(data) # 2 bytes(16 bits) per channel
+
+    print("* done recording")
+
+    stream.stop_stream()
+    stream.close()
+    p.terminate()
+
+    wf = wave.open(WAVE_OUTPUT_FILENAME, 'wb')
+    wf.setnchannels(CHANNELS)
+    wf.setsampwidth(p.get_sample_size(FORMAT))
+    wf.setframerate(RATE)
+    wf.writeframes(b''.join(frames))
+    wf.close()
+    return "Audio Recorded"
+
+if __name__ == "__main__":
+    app.run()
+