Month: March 2021

Ellen’s Status Report for March 13

This was a pretty productive week on my end. Over the weekend, I got Google speech-to-text working (made an account, got credentials, added in the code, etc) to great success! It just seems way more accurate than the other two options I had implemented originally. (This is based on the same little paragraph snippet Cambrea recorded on the respeaker for some initial testing.)

Also over the weekend (if I’m recalling correctly) I coded up our first version of speaker identification (the no-ML, no-moving version). At that point it was gratifying to see simulated transcript results with both speaker tags and voice-to-text!

And my final weekend task was preparing for the design presentation, which I delivered on Monday.

Speaking of design materials, I worked a lot on the design report document. Since I’m the group member who likes writing the most, I zipped through first drafts of a bunch of the sections which the others are going to proofread and modify for the final version. And in the trade-studies and system-description sections, I just wrote the technical bits that I was responsible for. It’s nice having this document pretty close to finished!

Finally, I started the meeting management module. This takes a new transcript update and actually updates the file corresponding to the correct meeting. I’ve finished most of it, except for the bits that interface with the database – I had to confer with the rest of the team about that.

In terms of the schedule, I’m kind of on-track, kind of ahead of schedule. I’m on track for writing the meeting manager (self-assigned due date of Monday), but as for my schedule item after that, “transcript support for multi-mic meeting,” I’ve actually been building that into the transcription the entire time, so it looks like I’ll be able to start my actual next task earlier than scheduled.

Next week I’m scheduled to deliver the meeting management module. The on-website meeting setup flow, which is my next responsibility, will also be partially completed.

Mitchell’s Status Report for March 6

This week I worked on the website development as well as the design review. I finished setting up the website backend so that it is able to integrate with other components like the transcript. I also worked on the design paper on reducing the mass of information that was put there 

Progress-wise, I am on track for the web end, but have not started the audio filter for the audio on the raspberry pi. I might have put a bit too much work simultaneously for this week. The tasks still have additional time on them, but just did not start on the desired time. Next week, I will work on finishing the design paper, starting the filtering, and website development.

Cambrea’s Status Report for March 6

This week I started finished the design for the network connection between the raspberry pi and the AWS server.  We are still planning on using a UDP connection here to transmit packets, the packets will be a struct of the micID, audio bytes and direction of arrival information for the audio.   The micID is assign to a single rapsberry pi audio device from the server, when the connection is first made.  The AWS Server will receive information that the audio device wants to connect and will return the mic ID, this will be resent until the raspberry pi audio device receives the micID or the connection times out.  On the server both listen and send will need to run on separate threads to make sure that the server is always listening.  There will also be a separate thread for each audio device connection to the server.

I set up a simple server code to test the connection between the aws server and the raspberry pi.  Since the AWS server is remote it is in a different wifi network so I had to set up port forwarding, so that the raspberry pi can access the server code on the AWS Server.  AWS has its own way for users to setup port forwarding, using AWS Systems manager, I have been following this tutorial https://aws.amazon.com/blogs/mt/amazon-ec2-instance-port-forwarding-with-aws-systems-manager/.

We also received the AWS credits so we upgraded our ec2 instance to m5a.large, we are tracking our usage of the server.

I am currently on schedule, In the next week I am going to complete the code to handle the networking from raspberry pi to AWS Server, this code will include the initial raspberry pi audio device handshake with the AWS server, and the transmission of our packets.

 

 

Team Status Report for March 6

This past week, our team finished up our design review presentation; we also went over the feedback from the project proposal presentation and incorporated it into our project. Cambrea set up the respeaker and created a set of test audio data. Ellen tested the ML software on the test data and eliminated one of the ML options that we considered, and investigated the other option that Abha, our TA, suggested. Mitchell continued working on the website.

Some parts of our schedule have been shuffled around earlier as we got our hardware earlier than expected, but according to our modified schedule, we are still on schedule. We did encounter a risk this week. We spent a time chasing models that are duds and we eliminated it by testing early and finding alternatives. One of the models had a 100% error rate. Design change wise, we also are adding a LED indicator when audio is not being picked up and audio preprocessing for the ML. Next week, we will be finishing up our design report and continue our first phase.

Ellen’s Status Report for March 6

This week I did a real mishmash of stuff for the project. I finished the design presentation slides as well as preparing my remarks for the design presentation, which I demoed for the team to get feedback.

I finished coding the transcript generator sans speaker ID — this included a multithreaded section (so I had to learn about python threads), audio preprocessing (downsampling to the correct rate and lowpass filtering), as well as going back through my previous code and making the data structure accesses threadsafe.

Since we received the respeaker mic in the mail, Cambrea recorded some audio clips on it and sent them to me so I could test the two speech to text models I had implemented in the transcript generator. The performance of DeepSpeech was okay – it made a lot of spelling errors, and sometimes it was easy to tell what the word actually ought to have been, sometimes not so easy. (If we decide to go with DS S2T, maybe a spelling-correction postprocessing system could help us achieve better results!) CMU PocketSphinx’s output was pretty much gibberish, unfortunately. While DS’s approach was to emulate the sounds it heard, PS tried to basically map every syllable to an English word, which didn’t work out in our favor. Since PS is basically ruled out, I’m going to try to add Google cloud speech to text to the transcript generator. The setup is going to be a bit tricky because it’ll require setting up credentials.

So far I haven’t fallen behind where my progress is supposed to be, but what’s interesting is that some past tasks (like integrating speech to text models) aren’t actually in the rearview mirror but require ongoing development as we try certain tests. I kind of anticipated this, though, and I think I have enough slack time built into my personal task schedule to handle this looking-backwards as well as working-forwards.

This week my new task is an initial version of speaker ID. This one does not use ML, does not know the number or identity of speakers, and assumes speakers do not move. Later it’ll become the basis of the direction-of-arrival augmentation of the speaker ID ML. I’m also giving the design presentation this week and working more on the design report. And by the end of next week, Google s2t integration doesn’t have to be totally done but I can’t let the task sit still either; I’ll have made some progress on it by the next status report.