Cambrea’s Status Report for April 24

Last weekend we finished the migration to AWS so now we are running our server code on the ec2 server, instead of running our server on the local computer.

Since we are now running our system on the server we started on our final standard tests.  I completed the 2 tests for the audio device to aws server networking  this week .

I first wrote the latency test, this test sends packets to the server and should receive the packet back.  I capture the timestamp of the packet when it is sent and compare this to the time stamp of when the packet is received back at the raspberry pi to make sure we use the same clock to calculate the latency.  After sending 500 and receiving 500 packets we calculate the average latency of the systems round trip time.   We are getting the results of 27-80 ms average latency which is below our requirement of 150 ms latency.

The second test is the dropped packet test, this sends packets to the server for a fixed amount of time and counts the number of packets received back from the server, we calculate the dropped packet rate as (number packets sent – number packets received)/ number packets sent.  We ran this test for 2 minutes and 10 minutes and found the dropped packet rate to be less than 1% at .2% and .1% .  This also meets our requirement of <5% dropped packets in our system.

These 2 tests are in this ClientTest.py, this file works with the arguments

-b  for basic connection to the server test, send and received 1 packet to make  sure the connection exists

-l  run the full latency test

-d run the full dropped packet test

the tests are in the corresponding functions testLatency and testDroppedPacket

ClientTest

This progress is on schedule we will be testing until next week as well.

Next week I will be working on the final presentation slides and the final report.

 

Team Report for April 24

We performed our demo last Wednesday and received overall good feedback from Professor Sullivan and Abha. We had some technical difficulties during the beginning of the demo, so we started to add some robustness to the routines. The issue occurred when there were two microphones connected to a meeting and one of the microphones was disconnected. We also worked on testing the AWS deployment as well as formalizing the testing scripts. We also added in the ability to download transcripts of the meeting as pdfs. We have also started working on our final presentation slides.

Since we are performing our tests now, if there are any major issues that require an implementation overhaul, we would have to scramble to accomplish it.

Below is an image of our contraption. It has a Raspberry Pi, microphone, ReSpeaker, and custom ReSpeaker case.

Below is an image of our meeting transcript of our meeting in progress.

Ellen’s Status Report for April 24

We’ve made a lot of progress the last two weeks. Our demo last Wednesday went well, and afterwards because of the feedback we received we decided to revamp the speaker id setup process to make it more user-understandable. I worked on doing that all weekend, and then we tested it a bit on Monday while testing the AWS deployment.

Then Tuesday and Thursday I worked on outlining and fleshing out our final presentation slides. I also came up with a potential method for transcript latency testing.

Since we’ve already moved on to validating requirements (with a high confidence that they’ll pass)  I’d say we’re on schedule according to our Gantt chart. This week we’ll finish gathering and evaluating test data. I’ll probably be calculating a lot of transcript related error rates. We’ll also prepare our final presentation and work on our video.

Updated Gantt Chart

Updated Gantt Chart Link

For our updated gantt chart, we have completed the task “Integration of RPi + Transcript + ML” half a week early so we are currently working on the task “Complete System Debugging / Testing”.  Our current “complete system” for the demo uses a computer as the intermediary server that handles the speaker ML and transcript streaming.  Ellen is working on some ML improvements for integration and testing at this time. After the demo Cambrea and Mitchell will start the migration to using the AWS Server instead of our local computer for the task “AWS Migration”.   For our latency testing and  standard testing we will complete these during week 12 and 13  after we have migrated to AWS.  We will start revising our final report the during week 11, and work on our final presentation and video during weeks 12-14.

Cambrea’s Status Report for April 10

Last week week I completed the streaming code and AWS server code that is responsible for sending and receiving  audio over the network,.  The ReSpeaker offers a capability of detecting whether a user is speaking, using the is_voice() parameter.   I was testing this capability over the last weekend and found that the output audio using this information is too choppy to be intelligible to the user.   We are currently testing if after we tag the packets as voice and feed those to the transcript, if these packets have enough data to create the transcript.

This week we started integration of each of the systems so we were working on campus in HH D level.  On Monday and Tuesday I added the raspberry pi’s to the cmu device wifi.  We were having issues with connecting the devices to the wifi so we reflashed the OS to the SD cards on the raspberry pi and reconfigured the wifi and it now works on the cmu-device wifi.

On Wednesday we finished the integration between the audio streaming on the audio devices and the transcript generation on the server.  For this integration we are currently using Ellens computer to act as the Server so that we can complete the integration for the demo before migrating to using the AWS Server.  We are currently developing the speaker Identification more to make sure that I works to recognize different speakers.

This week we will start the tests for transcript accuracy, prepare for the demo, and also start the migration to AWS.

Mitchell’s Status Report for April 10

This week I worked on debugging the transcript streaming and working in my group. The consumer was changed to be a fully  asynchronous model and the webpage now properly update when the transcript is fed in. There were also web socket instabilities that were debugged. For the group work, we met Mon., Wed., Fri., Sat, and will meet Sunday for multiple hours each time. I mostly helped to test the system and tweak changes like transcript autoscrolling.

From now on my schedule is the same as the group’s schedule. We think we have a demo prepared at this point, but we will perform further stress testing on the demo and mult-mic interaction.

Team Report for April 10th

Last week our team had finished our individual components, Cambrea finished audio streaming between raspberry pi and server, Ellen finished speech to text, and speaker identification ML, and Mitchell finished the database and website setup and transcript streaming.    This week our team has focused heavily on complete system integration and testing.  We have completed the connections between all components and have real transcript streaming to the website of the input audio from users.  We are currently working on improving our speaker identification during the setup phase of the meeting.

Our main risk for right now is that the direction of arrival data from the speaker can have a small fluctuation during the time that a speaker changes or during the time that a speaker is speaking.  This fluctuation is mainly affecting our setup phase for when we register speakers to be identified by the speaker identification ML.  During the setup phase we rely on the DOA to determine if a new speaker should be registered with the system.  We are currently fixing and testing this by only saying that a new speaker should be registered if a new DOA is detected and also if there is a significant amount of audio  coming from that direction.  This will work to ignore small fluctuations of DOA.

 

Updated Gantt Chart Link

https://docs.google.com/spreadsheets/d/1eeHeut41JF_Ju9Ys14n_sOLiZGYdpl4HtEale2ySasY/edit?usp=sharing

For our updated gantt chart, we have completed the task “Integration of RPi + Transcript + ML” half a week early so we are currently working on the task “Complete System Debugging / Testing”.  Our current “complete system” for the demo uses a computer as the intermediary server that handles the speaker ML and transcript streaming.  Ellen is working on some ML improvements for integration and testing at this time. After the demo Cambrea and Mitchell will start the migration to using the AWS Server instead of our local computer for the task “AWS Migration”.   For our latency testing and  standard testing we will complete these during week 12 and 13  after we have migrated to AWS.  We will start revising our final report the during week 11, and work on our final presentation and video during weeks 12-14.

 

 

Ellen’s Status Report for April 10

This week has been a busy one for our team! Over the weekend I wrote audio-accumulation for the UDP side of the webserver and accumulated raw audio instead of files. Then from Monday forward our focus was on integration. After every time we met I had a bunch of todo items to debug or just improve the server processing and transcript generator. We met Mon., Wed., Fri., and Sat. for multiple hours each time. My work has included small (but important) tweaks like an improved filenaming system, improved audio accumulation, improved error reporting, changing what does and does not count as a “speaker change” in the transcript and in the backend, and others I can’t recall at this point. Larger changes I made would be changing the way speech to text results are accepted from Google (only accepting “final” and not interim results) and adding more branches and states to the state machine of microphone setup.

From now on my schedule is the same as the group’s schedule as a whole. We think we have a demo prepared at this point but when we meet tomorrow we’ll do more testing on the demo we’re preparing as well as testing how two mics in the same meeting work together. By the weekend we’ll be done integrating and debugging our two-mic setup and we’ll have some idea of how well the transcript is matching our requirements/targets.

Mitchell’s Status Report for April 3

This week I worked on integrating the transcript streaming and fabricating a case for the respeaker. For the case, it was designed using VMWare Horizon to access the windows lab cluster and Solidworks to CAD the model. The transcript streaming was interfaced as a producer in the transcript updating phase in the meeting manager using asynchronous to synchronous communication to the channels layer. The consumer model will then append or rewrite the changed sections and push those updates to the web hook which will update the webpage.

At this point we’re sort of reaching the point of schedule ambiguity. I do not have any more individually-assigned tasks. I will be testing the transcript streaming further and try to get a full system that works locally on our computers.

Ellen’s Status Report for April 3

This week I worked intensively on the speaker identification subsystem. If you’ve already read the team status report you’ll know that we decided to ditch the speaker diarization packages we had previously identified as possible solutions – crucially, none of the packages provided audio streaming abstractions – and proceed with google cloud speaker diarization, which could be added to the current google speech to text request by simply tweaking a configuration variable. This created the opportunity to integrate the speech to text and speaker ID modules, so I had a lot of code to write and rewrite to both get the speaker ID module integrated and to add in the processing the way we desired it (including the DOA augmentation). Initial tests (using random vocal samples I found online) suggest to me that the system is going to work well and that DOA augmentation is actually going to be quite valuable – but I conducted this testing just yesterday, so the jury is still out, I suppose.

At this point we’re sort of reaching the point of schedule ambiguity. I would consider speaker identification to be finished as of yesterday, which is a good half-a-week early. I don’t have any more individually-assigned coding tasks. Now I’ll  be supporting integration through debugging and revision. As for what I know I want to accomplish this upcoming week: I want to rewrite the network-to-transcription queueing interface to accumulate multiple packets (if available) before sending the audio off to transcription. I also want to help Mitchell get transcript streaming fully working.