Can we ensemble fastText along with SVM?
In your use case you can as you're dealing with 3 models you should keep in mind that:

The models have different mechanics to use the predict() method: 

FastText uses an internal file (serialized model with .bin extension, for example) with all embeddings and wordNGrams and you can pass raw text directly;
SVM and NaiveBayes you're obligated to pre-process the data using CountVectorizer, TfidfVectorizer LabelEncoder, get the result, repass for the LabelEncoder and deliver the result. 
You will need at the end deal with different probabilities (if you're predicting with k > 1) and probably you need to take care of this

If you're going to serialize it to production you'll need to pickle the SVM and NB models and use .bin for FastText model and of course the embeddings from the former ones need to be instantiated too. This can be a little pain in your response time if you need to predict in near real time.
I'm trying to ensemble the three different models (FastText, SVM, NaiveBayes).
I thought of using python to do this. I'm sure that we can ensemble NaiveBayes as well as SVM models. But, can we ensemble fastText using python ?
Can anyone please suggest me regarding the same ...