Progress
This week I continued downloading necessary modules on RPi. I tried to run a small script to load the Google news word2vec model on RPi but the Pi always stuck probably due to the gigantic size of the model. I decided to switch to another word embedding method GloVe (Global Vectors for Word Representation), which has various models with different sizes and dimensions to choose from. I downloaded the GloVe model with 400,000 words and 100 dimensions, and I successfully loaded the model on the RPi.
I also implemented the modify button for each entry in the entry list page. This includes displaying the id for each entry and creating a new html page and a new function in views.py to handle modification action.
Schedule
I am on schedule.
Next Steps
Next week I will continue to implement the delete function for the entries and help with the audio input functions of our web app. I will also rewrite the word2vec script using the new GloVe model and incorporate the item classification into the NLP process.
Verification method
Test for manual input feature: To test that the basic manual input functionalities are implemented as expected, we will run our web app following the flow chart in our design report, covering all branches and corner cases of the flow chart.
Test for item classification accuracy: We will randomly select 20 item names outside our dataset, label them with their correct categories, and feed them into my item classification script built upon the GloVe model. We will then determine the accuracy of its prediction, which is expected to be 90% or above.
Test for latency: if the whole speech processing pipeline takes more than 3 seconds on average, I should consider reducing the number of dimensions of the GloVe model for less loading time.