optimumsuave

Developer of murph.live here again - after reading this thread I have some ideas I'd like to vet with everyone here.

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.

Hello!

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.

Hi lunixbochs!

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!

Thank you for the kind words. My team and I simply want to help people and create accountability/transparency.

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!

Hello Hacker News!!!

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!