Optimizing a transliteration model in Python

I need a help with optimizing a transliteration model in Python. Optimization has the following steps: 1. Use the Viterbi function based on the function implement forward_scores by adding the sum through a replace maximization(Viterbi) and remember which of the three values is greatest was. (Edit Distance) Then you have to implement the extraction of the best aligning. 2. Modify the function get_transliterations so that they are additionally calculated with the Viterbi algorithm the best alignment and returns this. This is then also output. 3. The transliteration system that must implement later can do not handle insertions. Therefore, you should next in because Alignments eliminated eliminate all inserts Insertion: z merged with the previous pair x: y to x: yz. If an insertion is at the beginning, she will instead use the subsequent couple merged. For example, the sequence becomes: x a: a: b : f c: c: d replaced by a: xabf c: cd. (N-grammmodell-training for transliterations) 4. Implement the transliteration system. It uses a NGramm model and thus takes into account context information in the Contrary to the mining model. The parameters of the NGramm model become off estimated previously calculated Viterbi alignments. For that use the program [login to view URL] as a template. 5. Endstep is a program that defines the parameters of the NGramm model reads in and the list of Transliterative word pair candidate filters: For each word pair becomes the probability of the best transliteration and the Probability of the second element of the word pair calculated. There are pseudocode for almost every step and even model for optimization. The entire project needs to be completed within two weeks, there should be deliveries of work in progress at least every two to three days (the steps as displayed)