That being said, transcription still isn't at the point where it can understand radio garble.
> because I'm going to take that group that someone over the Willing. Can you raise Lieutenant lady and having semi-open Buddhist month
> you know the carrot for you and Hudson no accident
Reminds me of the early days of Google Voice voicemail transcription.
Thanks for sharing!
* Put the audio clip next to the transcribed text
* Flag transcribed text which fails some heuristics
* Allows users to suggest edits to the transcription
* Train the transcription models on contributed edits + police specific lingo
I'm the owner of Broadcastify.com, where presumably these streams are being transcribed from. We've dabbled in this space and looked at real-world approaches to taking something like this to market, but transcribing 7000+ streams to text seems like an expensive (computational) and ($$) effort that needs a lot of investigation.
Note to mention that the individual lexicons between streams are drastically different.
I wonder how the developer has done the integration to our streams... because I never heard from them :)
It's not currently streaming any feeds because Google Speech is pretty expensive, but I have the expertise and plan to train my own speech models that would be less expensive to run and more accurate than Google at this task as well.
The main difference between this and murph is my `feeds` site has UI for users to listen to the audio and fix/vote on transcriptions, and corrections are propagated quickly to other users.
This is close enough to the Seattle feed that you can do a compare & contrast.
Heard: "clear my first call ocean nora 470" On the site: "charlie my first call"
So, yeah, this still has a long long way to go. I considered and discarded this in 2011 because it was pure insanity, and as another comment suggests, it's highly context-sensitive.
"ECR" is El Camino Real. "Vets" is Veterans Blvd.
But...
"Code 99" is the emergency button... for one department... and it means something else entirely for another, just 20 miles apart.
I'd love to have it, but it still seems out of reach.
My plan was to collect user transcription corrections on my site then train my own inexpensive models on them. The open-source speech tech I work on can do passable transcription at close to 100x faster than realtime on a quad core desktop CPU (or 200 simultaneous streams per 4-core box at 50% activity). With higher quality transcription it's closer to 10-20x faster than realtime.
For your case you could also try to push some of the computation down to the uploading machine. These models can run on a raspberry pi.
I think the biggest work for a new effort here is going to be building local language models and collecting transcribed audio to train on. However, there have been a couple of incredible advances in the last year for semi-supervised speech recognition learning, where we can probably leverage your 1 year backlog as "unsupervised training data" while only having a small portion of it properly transcribed.
The current state-of-the-art paper uses around 100 hours of transcribed audio and 60,000 hours of unlabeled audio, and I bet you could push the 100h requirement down with a good language model and mixing in existing training data from non-radio sources.
We're working on client ingest models now that work on more of a "tasking" perspective, where someone deploys a device that is GPS enabled and then we send an ingest task to fill in coverage, start new coverage, etc. But this is predicated on low cost ingest devices (read: RPi and RTL sticks) which might not have the horsepower needed for transcription at the client level.
"movie up and around pain and dreamy I'm not"
or
"happy person that you can just so may I help you"
https://rogueamoeba.com/loopback/
Someone clever enough could create containers to run the software locally and have many loops running off many streams to many instances of the audio to text feature.
I'd have to imagine that stream listening follows some sort of power (or otherwise 80/20) law, so hopefully that would help with the expense?
https://wiki.radioreference.com/index.php/Broadcastify-Calls
I know OFCOM (UK FCC) state it's illegal to listen to certain frequencies without permission, which seems delightfully unenforceable (for legal reasons I most definitely do not possess a wideband software defined radio or a similarly sensitive antenna)
I deal constantly with nefarious developers who often feel like Broadcastify has an obligation to grant them a license. We had a terrible dust up this weekend with a developer that was upset we wouldn't grant him a license to develop "yet another police scanner app" and he went on a warpath with me personally.
The app store environment is a major pain and exhausting...
There's apparently some uncertainty around handling of encrypted emergency services communications: https://www.rtdna.org/content/scanners
https://www.fcc.gov/consumers/guides/interception-and-divulg...
I wonder if one could mix in openstreetmap data for a location to help pick up local references. (Eventually would be cool to round trip it with a little ping when addresses/businesses are referenced).
Until a system can train right down to the officer/dispatcher I don't see how this gets beyond 50-60% accurate. I can't even transcribe half the calls, not sure why I should expect a computer to (at this time anyway).
Any thoughts on adding the ability to comment/transcribe/etc?
Oh shit.
Sorry, talking about the project this thread is linked to.
I selected the NYC scanner and found many examples like this:
June 8, 2020 9:03 PM EDT: "Google Launcher new job and I want to play better third-party colder or does the people from the vegetable okay"
For instance:
> at the beach view new screen for assistance there is a needle in his hand he's foaming from his mouth throwing off this item
What the officer actually said on the radio:
> He was going to Rainier Beach area. A request for assistance to approach two people with needles. <operator>: Call was from a neighbor in the area.
> Please don't comment on whether someone read an article. "Did you even read the article? It mentions that" can be shortened to "The article mentions that."
Yes, I looked the website, and I also have my own website that does the same thing in a very similar manner. My comment was entirely in response to the suggestion Mac speech recognition be used for this. It should not be used for this. Based on previous experiments I have personally performed, it would be even worse than the website's accuracy. I then pointed out what a good solution might look like (and neither my website nor the linked website do the good solution yet)
I should be clear that I know there's tragic racism and other injustices everywhere, and that problems tend to be worse than is visible to most people, and I think now is the time to finally right some wrongs that we should've a long time ago. Perhaps relevant to that, it was really reassuring to observe signs of goodness in the institutions that many people never have occasion to. Problems need to be solved, but there's hope from multiple directions.
> Jun 9, 2020 12:17 PM
Sounds like it's getting them very wrong.
Yes, but it takes a bit to build up that weighted list and it can be quite hefty to parse. So they may be building this behind the scenes currently. As another commenter pointed out, being able to correct a chunk and send it back to help the algorithm would be a nice feature here.
Side note: I'm dealing with this issue at the moment - if anyone has a good resource on reducing the workload I'd love a link!.
Ed:spelling
I am the developer of murph.live - I just want to thank all of you for taking the time to check it out and give us excellent feedback. I stumbled upon this post and now have goosebumps.
This started by listening to police scanners throughout the night during recent protests in Seattle, WA. I wanted to help and I immediately put my credit card down for Google's Speech to Text API.
As for the inbound streams, @blantonl is spot on - we use the streams from a premium account on broadcastify.com (thank you for not sending a cease and desist yet!).
A few dockerized ffmpeg processes segment the streamed audio into 30 second wav files. Subsequently, sox removes silence from the audio files as police scanners have quite a bit of downtime between transmissions. The performance is very scalable using docker containers to record and trim.
Currently, we pipe these trimmed wav files to the Google Speech API - as others have mentioned this is $$$. We are receiving donations, but this dependency on Google will eventually need to be eliminated.
I have started looking into possible solutions using NLP and other acoustic models to bring the costs down. Honestly, speech processing is not my forte so I'm kind of shooting in the dark here. I am currently testing pre-trained models for wav2letter++, kaldi, vosk, and maybe deepspeech.
We can all agree the quality of the transcripts is something to be desired and improved upon. Potentially dangerous if transcribed incorrectly, but nonetheless we wanted to launch to give citizens a platform to provide transparency into our government. The idea is what counts right now.
Thanks again and I will be responding to a bunch of comments on here! You all rock!
We decided to ask for forgiveness on this project - we do in fact have a premium account with you guys! ffmpeg records and segments your streams, sox removes silence from scanners, Google transcribes, redis serves it all up quickly!
A broadcastify.com premium account is not not a great excuse, but I would love to have a conversation at length with you and your team! How do we get in contact? Thanks again!
https://news.ycombinator.com/item?id=23322321
at 33 second mark https://twitter.com/jamescham/status/1265512829806927873
"foaming at the mouth" was never even close to being uttered on the radio. I'm guessing the (flawed) model inserted that part because of the proximity to the word "needle" and "assistance".
Maybe? No idea.. this website it totally fucked.
Your prototype is amazing! The quality of transcription is definitely better than ours via Google.
After we did some legal research we wanted to avoid storing the recordings and rather solely transcription text. Giving access to a platform for humans to verify the transcriptions and in turn train the model is a great idea.
I have started working on getting some pre-trained models set up. I am trying to implement them with wav2letter, deepspeech, kaldi, vosk, etc. - I just need to be pointed in the right direction.
Raspberry Pi's were something I was considering as well - small energy footprint and powerful enough to run these models.
Do you have any advice on ML or acoustic models to avoid? I am working with the 100 hour dataset now.
Thanks!
The quality is currently limited by Google's API. I am working on getting some pre-trained models implemented, but voice processing is not my speciality as a software engineer.
I do NOT want to spread misinformation nor do we want to unjustly slander anyone. Tonight I will be adding a disclaimer mentioning the limitations of our service and will make sure it is forefront on the website.
Hopefully we can create a model which can deliver better results.
1. We need to post links to the source of the stream. I neglected to do that for fear of cease and desist, but now I realize we need to create accountability on our own platform. I will be contacting broadcastify.com to ensure we can direct users to a source.
2. We need a disclaimer on the site directly in your face. I agree with everyone here - this could potentially spread misinformation and do more harm than intended. These transcripts should be read with caution. Additional messaging from us is a must.
3. We need a better acoustic model. Google is too much $$$ and although I'm an engineer, I'm not a linguistics machine learning expert. Can anyone help me with this please?
Our mission was to create transparency into our government - not cause harm. There is a lot of responsibility creating a tool like this and we want to get it right.
With that being said, this site blew up in a few hours. I'm overwhelmed. Please let me know if you'd like to help. Thank you to everyone for the feedback so far - it all helps immensely.
I’ve had some decent results with the following:
I have to research how to hand tag my own samples to see if that offers significant accuracy improvements (let’s say I want to accurately transcribe one voice consistently).
Google and Watson APIs are not too free, and I believe Watson has a audio length limit (possibly limited by free tier, or possibly limited in general for all tiers).
Cool to see some real world attempts using this stuff.
Typically you would use/train a LM for your domain or specifically for your dataset.
I don't know the law in the US, but here in Sweden any police investgiation is kept under strict secrecy until completed. When a crime occur and journalist ask for details the answer is always the same. While investigation is ongoing no details may be given. That would not work if there is a searchable transcription of the communication online.
So an nice technical achievement, but the more successful it is the faster it will be made obsolete in terms of getting information out of police scanners.
Otherwise, you do run the risk of "See the screenshots - the police transcriptions have been changed to become police friendly now!"
This got me thinking: What if we get the police radio transcripts, and extract every mention of an address. Then have a central hub of drones that get dispatched automatically to any address of "interest" filming.
It's a bit crazy, most likely illegal. But definitely would be cool to see! Arguably truly unbiased "reporting". If you want to know the situation, just plug in to the feeds and get a decent overview of how it looks.
I have 30ish streams and keep 6 days worth, I could keep longer if you'd like to work together on this. I reached out to some of the people above, the Broadcastify guy for example, and they are, as mentioned, ready doing their own thing so didn't really care about what I wanted to share.
I will say though I recently noticed that during the recent unrest in my city that the stream had a caption along the lines of 'police are using encrypted channels' which is understandable but disappointing from the perspective of a citizen looking for transparency.
I realize both the possible legal and technical difficulty of implementing something like this but have you had any conversations about how to maybe combat this? Times of unrest are not only a massive opportunity for Broadcastify to grow its user base but it's also when transparency is at its peak importance.
I seriously looked into doing this myself, but for this reason and costs I bailed.
Name, address, date of birth, social security number, identifying features i.e. height, weight, hair color, etc.
Don't underestimate how valuable this information can be.
Not to mention the movements of police resources and personnel.
All said published over the wire, anyone can hear with the right hardware/software.
Listed frequencies by federal/state/county/local departments
Searchable for all.