Author: ntoldala

This week I was focused heavily on two things: aggregating all of the datasets and material I will need for development and getting them uploaded on my remote development environment (AWS), drilling down into the metrics, requirements, and detailed design of our whole system for the design review. On the development side, I was able to retrieve and store the latest wav2vec 2.0 model for feature extraction specifically targeting mixed language inputs. Marco and I will be able to further fine-tune this model once we reach system-level training of the ASR model. I was also able to register and am waiting for final approval to gain full access to the SEAME code-switching dataset for Mandarin and English. Marco was granted access within a day or two so I plan to have full access to that data by Monday. My final setup for my remote Jupyter notebook is also fully configured. Using dedicated GPU instances, we’ll be able to train continuously overnight without having to worry about up-time interruption.

On the design side I completed detailed design documentation for each of the modules I will be either entirely or partially responsible for (Modules 1, 3, 7) with traceability matrices for requirements, unit testing, and validation. Each requirement can trace upwards to design-level requirements, and downwards to a specific test for easy tracking of how lower level decisions have been informed by high-level use case targeting. I added all of these matrices along with system-level versions of them to our ongoing design review document which also includes module-level architecture descriptions and interface details between modules.

I’m currently on-track with my planned work. Since Marco was able to gain access to the SEAME database, it has freed both of us up with an extra two-days in the schedule for either training work or system integration work at the end of the semester. This week I will plan to finish our design review presentation, our design review document, and target having a first version of the LID model initialized and ready for initial training by next weekend.

This week I worked on getting AWS configured for the DL language model we intended to deploy. I made resource requests for AWS credits and a limit increase of GPU instance types. We plan to use G-type instances for most development, though we may deploy some p-type instances for especially heavy system-wide training in later stages. I was able to download and setup the latest version of Jupiter Lab for remote development and was able to ssh in properly to my first instances configured with an AWS Deep Learning AMI. I experienced some issues ssh’ing in originally so I spent some significant time re-configuring my AWS security groups and VPC with the correct permission to allow my to access the servers now and with any future instances we may launch.

Progress is on track currently. We are currently ahead of schedule on our implementation as the official period does not begin for at least another week and a half and we’ve already had success with several early steps of development. This week I will also be focusing heavily on making and documenting key design decisions in detail. These will be presented next week at the design presentation which I will be conducting.

There will be several major things I plan to complete by next week. I’d like to have finalized detailed architectures finished for several versions of the LID or ASR models. There are a couple of different formulations which I’d like to experiment with. Marco and I will also need to finalize the actual task division we’d like to use for developing the sub-models of the overall system. This way he and I will also be able to document and finalize the different datasets we may need to compile or augment for module-level training. By next weekend we should have small development versions of both LID and ASR models running on remote instances and completely ready for further training and development.