Problems with shape of Conv1D on Keras
Just tried your cnn_model function and it works correctly for me.
Not sure about what your problem is, but it seems to be your X and Y. X must not have shape (2023, 65), but shape (datasize, 2023, 65). and Y should have shape (datasize, 1) as well. otherwise your network treats X as 2023 inputs of shape (None, 65) instead of treating it as n inputs of shape (None, 2023, 65).
here is my code in case you wanna run it (important part is the definition of X(ims) and Y(lab) :
import keras
from keras.layers import Conv1D, Dropout, MaxPooling1D, Flatten, Dense
import numpy as np

ims = np.random.randn(100, 2023, 65)
lab = np.random.randn(100, 1)

def cnn_model(X, Y):
    model = keras.Sequential()
    model.add(Conv1D(filters=64, kernel_size=3, activation='relu'))
    model.add(Dropout(0.25))
    model.add(Conv1D(filters=64, kernel_size=3, activation='relu'))
    model.add(Dropout(0.25))
    model.add(MaxPooling1D(pool_size=2))
    model.add(Flatten())
    model.add(Dense(64, activation='relu'))
    model.add(Dense(1, activation='sigmoid'))
    model.compile(loss='mse', optimizer='adam', metrics=['mse', 'mae'])
    print('training')
    model.fit(X, Y, epochs=10, batch_size=32, verbose=0)
    print('finished training')
    model.evaluate(X, Y)
    return model


m = cnn_model(ims, lab)
m.summary()