model.py

import os
import csv
import glob
import keras
import pandas
import librosa
import numpy as np
from numpy import loadtxt
from scipy.io import wavfile
from keras.layers import Dense
from keras.layers import GRU
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.optimizers import Adam,RMSprop
# from keras.utils import np_utils
from keras.layers import Dropout
from sklearn.model_selection import KFold
from sklearn.preprocessing import LabelEncoder
from keras.layers import BatchNormalization, Bidirectional
from sklearn.model_selection import cross_val_score
# from keras.wrappers.scikit_learn import KerasClassifier
from tensorflow.keras.callbacks import  ModelCheckpoint
from tensorflow.keras.models import Model, load_model
from sklearn.model_selection import train_test_split

input_shape=(64,40)
model = Sequential()
model.add(GRU(units=256, dropout=0.05, recurrent_dropout=0.35, return_sequences=True, input_shape=input_shape))
model.add(GRU(units=768,  dropout=0.05, recurrent_dropout=0.35, return_sequences=False))
model.add(Dense(10, activation='sigmoid'))