Create timeseries dataset for TensorFlow v2 - tensorflow

Question

I'm trying to feed a model CNN+LSTM with data from a csv. What I'm missing is setting a correct dataset to start training my model.
This is my test model:
def test_model():
model = models.Sequential()
model.add(TimeDistributed(Conv1D(32, 4, strides=1, activation='relu', padding="valid"), input_shape=[None, 6, 20]))
model.add(TimeDistributed(MaxPooling1D(pool_size=2), input_shape=[None, 6, 20]))
model.add(TimeDistributed(Conv1D(64, 4, strides=1, activation='relu', padding="valid"), input_shape=[None, 6, 20]))
model.add(TimeDistributed(MaxPooling1D(pool_size=2), input_shape=[None, 6, 20]))
model.add(TimeDistributed(Flatten(), input_shape=[None, 6, 20]))
model.add(LSTM(100, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(100, activation="relu"))
model.add(Dropout(0.4))
model.add(Dense(5, activation="softmax"))
model.compile(optimizer=keras.optimizers.Adam(1e-3), loss="binary_crossentropy", metrics=["accuracy"])
return model
Here you can download test.csv
CSV dataset is made of 6 features + 1 multilabel. I need to create a time series each 20 row, so I think my shape should be [None, 6, 20] ordered by ascending timestamp value.
I'm new in TensorFlow and I don't know how to create an appropriate dataset from scratch, I was able to load a dataset from directory with images (for CNN) but in this scenario I really don't know how to do it.
This is what I tried to generate my dataset:
with open('test.csv') as csvfile:
dataset = list()
reader = csv.reader(csvfile, delimiter=',')
next(reader)
count = 0
timeseries = list()
labels = list()
for row in reader:
count = count + 1
if count <= 20:
timeseries.append(
[float(row[0]), float(row[1]), float(row[2]), float(row[3]), float(row[4]), float(row[5])])
else:
dataset.append(timeseries)
labels.append(int(row[6].split("L")[-1]))
timeseries = list()
count = 0
After that I transformed it in a tf.DataSet like this:
dataset = tf.data.Dataset.from_tensor_slices(dataset)
labels = tf.data.Dataset.from_tensor_slices(labels)
Here I got a <TensorSliceDataset shapes: (20, 6), types: tf.float32> like I want. Then I fed a K-Fold with it:
estimator = KerasClassifier(build_fn=test_model, epochs=60, batch_size=5, verbose=0)
kfold = KFold(n_splits=10, shuffle=True)
results = cross_val_score(estimator, dataset, labels, cv=kfold)
When running cross_val_score I got this error:
TypeError: Singleton array array(<TensorSliceDataset shapes: (20, 6), types: tf.float32>, dtype=object) cannot be considered a valid collection.
What I'm missing?