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from flask import Flask, request, jsonify
from werkzeug.utils import secure_filename
import pandas as pd
import numpy as np
import os
import time
import sys
from tensorflow import keras
#from keras.models import model_from_json
#from tensorflow.python.keras import optimizers
import os
import random
import librosa
emotions = {0: 'female_angry', 1 : 'female_calm', 2 : 'female_fearful', 3 : 'female_happy', 4 : 'female_sad', 5 : 'male_angry', 6: 'male_calm', 7 : 'male_fearful', 8 : 'male_happy', 9 : 'male_sad'}
#model = tf.keras.models.load_model('model.keras')
def make_predictions(inputfile):
cnn_model = keras.models.load_model("cnn_model2.h5")
prediction_data, prediction_sr = librosa.load(
inputfile,
res_type="kaiser_fast",
duration=3,
sr=22050,
offset=0.5,
)
mfccs = np.mean(librosa.feature.mfcc(y=prediction_data, sr=prediction_sr, n_mfcc=40).T, axis=0)
x = np.expand_dims(mfccs, axis=1)
x = np.expand_dims(x, axis=0)
predictions = cnn_model.predict(x)
emotions_dict = {
"0": "neutral",
"1": "calm",
"2": "happy",
"3": "sad",
"4": "angry",
"5": "fearful",
"6": "disgusted",
"7": "surprised",
}
return emotions_dict[str(predictions[0].argmax())]
# json_file = open('model.json', 'r')
# loaded_model_json = json_file.read()
# json_file.close()
# loaded_model = model_from_json(loaded_model_json)
# # load weights into new model
# loaded_model.load_weights("Emotion_Voice_Detection_Model.h5")
# print("Loaded model from disk")
# #opt = optimizers.adam(lr=0.00001)
# # evaluate loaded model on test data
# loaded_model.compile(loss='categorical_crossentropy', metrics=['accuracy'])
# def data_preprocessing(filename):
# X, sample_rate = librosa.load(filename,duration=2.5,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)
# featurelive = mfccs
# livedf2 = featurelive
# livedf2= pd.DataFrame(data=livedf2)
# livedf2 = livedf2.stack().to_frame().T
# twodim= np.expand_dims(livedf2, axis=2)
# return twodim
# def make_prediction(df):
# pred = loaded_model.predict(df,
# batch_size=32,
# verbose=1)
# preds1=pred.argmax(axis=1)
# abc = preds1.astype(int).flatten()
# return emotions[abc[0]]
app = Flask(__name__)
@app.route('/')
def index():
return jsonify({'message': 'Welcome To Speech to Emotion'})
@app.route('/api/media-file', methods=['POST'])
def predict_emotion():
audiofile = request.files['audiofile']
filename = secure_filename(audiofile.filename)
temp_path = filename
audiofile.save(temp_path)
#twodim = data_preprocessing(temp_path)
res = jsonify({'result': make_predictions(temp_path)})
os.remove(temp_path)
return res
if __name__ == '__main__':
app.run(debug=True) |