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- MCP server with stdio transport for local use
- Search episodes, transcripts, hosts, and series
- 4,511 episodes with metadata and transcripts
- Data loader with in-memory JSON storage

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Episode: 3446
Title: HPR3446: Speech To Text
Source: https://hub.hackerpublicradio.org/ccdn.php?filename=/eps/hpr3446/hpr3446.mp3
Transcribed: 2025-10-24 23:35:03
---
This is Hacker Public Radio Episode 3446 for Mundy, the 18th of October 2021.
Tid's show is entitled, Speech to Text. It is hosted by operator and is about 23 minutes long
and carries a clean flag. The summary is, I talk about converting HPR, audio to text and tagging.
This episode of HPR is brought to you by archive.org. Support universal access to all knowledge
by heading over to archive.org forward slash donate.
Hello, everyone. Welcome to another episode of Hacker Public Radio with your host operator.
Today I'm going to be talking about analyzing audio and extracting text out of the audio
and creating sort of a keyword database. The idea behind this is to have transcribed
audio from HPR episodes, for example, hearing impaired, whatever, and then also for metadata
and or tagging. The idea here is to eventually use natural language processing,
which is this sort of AI-driven artificial intelligence or whatever you want to call it,
way to analyze audio and pull key terms out of it. So I'll go over some examples in my approach.
This probably spent maybe an hour, an hour, and 30 minutes piecing this all together
and a quick little hacked up batch job or whatever. The first step is obviously
downloading the audio file itself. The second step is pulling that audio in to VOSC, VOSK,
and from what I understand these are all open source tools. I don't know what their licenses are
or whatever, but this is a proof of concept. We could kind of use anything. There's probably
better things for other stuff, and I'll go into that a little bit more. But I'm just merely
scratching the surface of all this. So the second step is, you know, after you've downloaded the
file, I'm using YouTube BL, converting that to WAVE and letting embedded YouTube BL FFN
pick, I think that's embedded inside of it or some magic to convert that to a WAVE file.
Now after that WAVE file is, the MB3 is downloaded and converted to a WAVE file. It passes through
this Python script called VOSK VOSK. And I'm just using the defaults now.
From what I can tell, it's built on like an input word list. So if the word is not in the word list,
it's not going to pull it out as a word. So for example, we got fight covert viruses instead of
fight COVID viruses. So what I'd like to see is it's actually recognizing words based on
not a word list. So we can't possibly put in every single word that's ever going to speak
been spoken in the English language. We need to pull out words that are made up, for example,
Carolina Con or Poned. And there should be a minimal effort around training the speech,
or speech recognition to pick up new words or get really close to those words.
So I don't know what that approach is going to look like. And I'll take any suggestions around
that space. But for this example, it looks like the way that VOSK works is that it's using like
an input word list. So if it's not in the word list, it gets something whatever closest to the
word. So the closest word to COVID is covert. Now, with the sample test sample python script,
you could probably just have it open ended and not import, you know, the word list and have it
kind of make up its own words. So phonetically typing words out. I'm not sure exactly how text
to speech or speech to text works in the in the event that the word doesn't exist. You know,
it's a made up word or it's a word that's not a common dictionary word, right? So that's the first
struggle or point I'd like to make that, you know, I need a way to extract the the words out of
the audio. And it's those words might be made up in some cases. So for example, maybe I don't know
what the match threshold is. You could say, okay, if the threshold is doesn't match to 60% or
greater, then use the word that you think phonetically is being said. So COVID might be
spelled incorrectly, but it might be at least close enough to where you could run that through
a spell check program. And then maybe it would spell auto correct it to to to COVID. So it doesn't
have necessarily have to be perfect. But from this, you know, from the standpoint of words that
don't exist, if you ran that through like a, for example, Google word lists or spell check algorithm
that was cloud based, that would have every single word in. Then we could take that word that
looks like COVID phonetically and it would auto correct to COVID. So that's how we could get around
the words that aren't words situation. So that next step is normalizing the output and removing
common beginning phrases, right? So in the beginning of every episode, you have the, you know,
the prologue and the whatever, whatever. So you have the impart in the beginning part.
We want to trim that out. We don't need the, you know, this episode and tonight's shows entitled
or brought to you by. We want to filter those out because we don't want those keywords popping up
when we do the analysts later. So we kind of don't realize this this output. It's in like a JSON or
XML. Once that output is normalized, it's then passed through the YAK, YAK, it's like yet another
something engine, whatever. So that part, you can set the number of, excuse me, kind of terms
to reply back to. And I set it to 100. It defaults to, I want to say 10. So I'll give you some example
words here. Let's see if I can find. Now, an episode about the latest episode as of today or whatever,
normal layer modes, erase merge and split. Look at layers, models, and gimp. So this is by Huga, I think
who am I looking at? Bobbie, Bob, Bob, yeah, Huga is going over some gimp stuff. And this is
episode 3, 4, 2, 0. So we can go in here, find our 3, 4, 2, 0, 3, 4, 2, 0, YAK out.
And for our words, we get 82 words or 82 phrases or whatever. And some of these phrases are
toy image layer, top layer transparent, remaining layers, modes, undocumented layer modes, layer
mode set, normal layer mask, open font license, layer mask effect, layer mask worked, layer group
move, layer mask situations, layer group put, layer completely transparent, layer windows maker.
So you're sort of building a multi word keywords list in here. So it's not exact transcribe.
It's a pulling out what they'll call keywords is it a keyword extraction, which may include one
word, it may include multiple words. And I'm sure how to break that up either. So there's the
detecting the actual words part that needs to be ironed out. And then there's the keyword
extraction, which I'm thinking I need to move away from keyword extraction using this method
and move something to text classification, which means I'm thinking it's just
categorizing text into key thematic things, the idea being that would be the tags for the show.
So we would get the text and we would run it through maybe a spell checker and it would
you know create a nice clean output or transcription of the of the episode. Then that gets run
through this AI thing and it spits out tags or one letter one word tags or maybe multiple word tags,
whatever we decide on. But the key there is getting this keyword extraction right or what I'm
thinking we actually need text classification. So given a group of words or sentences or text,
we want to pull out the classification. What is the theme? What is what is what are we talking
about? What is the classification? So I'm thinking text classification might be where we want to go.
But like again, just barely scratching the surface of the stuff, there's a couple of pieces of
software that are open-source that I've been looking at. And they're fairly complicated. You know,
when you start getting to machine learning and all this AI, whatever, and training, I don't want
to have to train it. I want to use existing modules or whatever. But at the end of the day,
I'd like to have a group of words that will basically make the episode have its own tags.
So for this episode, you know, it doesn't have the word gimp in it. But it does have, you know,
layer group out, layer completely transparent. You've got stuff like layer masks. You've got words like,
let's see, picked full saturation, full saturation spectrum, normal layer, image layer,
layer transparent, layer opaque, layer completely. So there's keywords in here that would aim you
towards the theme of the episode. But this doesn't tell me that this episode is about gimp and
whatever. Now that's what the title does, right? So we have at a high level what the title is.
We have a high level of what the notes are because usually people put something in the show notes
section that indicates what's going on, but usually it's links or whatever. For me, at least, I don't
put a whole lot in there. To be honest, my episodes don't stay down their own because they're
generally time sensitive. So, you know, in 15 years episode about whatever program is not really
going to matter all that much as far as, you know, the usefulness of that information. So
the idea there is, I feel like if we can get it to a point where it's giving us, you know,
a set of 10 words or key phrases that will kind of automate the show notes part of it. So,
show notes don't exist or maybe we add kind of a metadata field and attach that to each episode.
We could have more of a searching index where, okay, you've got the output. You can search just the
straight up transcribe output of the episode. And again, that's can be like for the hearing impaired
or whatever. You can download the text and yeah, it's not going to be 100%. But it'll get you,
you know, 99% the way there. And then the second piece is, you know, if the show notes are lacking or if,
you know, you want tags, we can use some AI and some stuff like that to bring it into, you know,
maybe a sort of a hodgepodge of show notes that would just have a bunch of phrases of words.
And that would help you identify, okay, well, yes, this is about Gimp, but it's about, you know,
compiling your own Gimp and you'll see the word compile and you'll see the word download and you'll
see the word Linux and you'll see the word, you know, cross compile and you'll see the word make,
right? And you'll be able to look at those words and understand that it's not about using Gimp. It's
about programming in Gimp or compiling Gimp or writing Gimp plugins, right? So, the idea there is
that, you know, you have just transcribe pull transcribe. Once we go from transcribe, we're going to
marinate that and basically have a list of 10 words. What I want is what I imagine future state
is a list of 10 words that describe the podcast by itself that will give you an idea of what
that podcast is about. So, that's like the second layer and then the third layer is create
automating that creation of tags. So, once we get kind of those key words or key phrases
into the mix, we can take those key words key phrases and maybe compare them to maybe Google
searches or compare them to something else and then the results based on that search will give us
potentially a key single keyword words to go through. So, for example, if we get feed it a list of
10 key words like layer mask worked, layer group move, layer mask situation, you know,
saturation, layer group put, layers window make and we put that all of kind of in quotes and we've
sent it to Google and maybe we pulled the top 10 results from that. Then we use keyword extraction
based on that to say, okay, these words are only found in, you know, out of all these search results,
the key words that come out are word gimp. So, this must be about gimp and maybe some,
maybe we can use some other search pattern recognition, you know, here's a list of things.
What am I talking about? And that's what we want to, we want to answer programmatically and say,
okay, here's a list of phrases or a list of words. What am I actually talking about without,
without having to read the whole transcription, the reader or the listener should be able to
look at the kind of keywords or tags and tell what it's about. So, again, oh, I'm going off
here. So, once we've got the keywords in those tags, then we can kind of where people haven't
provided tags, we can provide our own tags in our own keywords or key phrases. So, at the very
least, I should be able to convert all HPR episodes and to transcribe them pretty successfully,
probably to, you know, a 95% or 98% accuracy. So, I can do that right now with something else.
I don't know if this is not the right, this vask or vask or whatever, it's not the right tool
for that or how to configure it differently. But at the very least, I can transcribe, get us
transcriptions. What I want to do is, again, add, you know, keyword phrases and then eventually add
tags and have that all automated. So, if someone does provide tags or they would like, you know,
the option to have automated tags and then they click next and then maybe they get an email that says,
please review your tags. You know, what do you, you know, if you think, you know, if this is great,
click yes, if they know or something. Or you can just have it, you know, done off line somehow
and I can provide a single binary. I don't know, there's ways to do it. But the idea there is,
you can use, well, a lot of these, especially these deep learning apparatuses and these
artificial intelligence things, you can use GPU. So that's also an option. The reason I say that is
an hour of audio takes about 20, 30 minutes to be analyzed. Two minutes of audio takes about 10
minutes or sorry, 10 minutes takes about two minutes to kind of analyze. So there is a time,
there is a time factor there. So each episode is going to be about 10 minutes or about two minutes
to analyze. And then you have your longer episodes that are, you know, like an hour or even, you know,
these giant six hour ones or two hour ones. There's a time element involved, but we can use GPU
for that if we need to use to do that later. But I don't think that's going to be a problem because
they trickle in, right? We only have one every day and if it takes, you know, it takes two hours to scan
one, one file who really cares. So the aspect from the speed perspective, I'm not too concerned.
But if we need speed, we can utilize GPU. We can utilize multithreaded downloads. We can
utilize the server itself, right? For running or maybe remotely mounting the audio files so that
we don't have to download them in text, you know, archive that org and HPR site itself, whatever.
So that's one thing. I already talked about the word input word stuff. So, you know, it said the word
COVID was covert and that's something, you know, I can work through. But I just wanted to get you all
thoughts. What you think has anybody approached this? I know I've talked about it before and I brought
it up before and I don't know if I followed the thread or if anybody answered, but really, for me,
at the very end of the, at the very least, I would like, I would like to help provide
transcriptions for episodes. And that's the biggest thing for me. The second thing would be to
help with tagging keywords with automation, right? So here's an example. I'll provide links in
the show notes to episode text and examples. And again, this is this is proof of concept stuff.
This is just an example and all of this can be tuned. We can use different software.
If you, if we find out that you guys like it, feel free to let me know. It's free load,
if I read L-O-A-D-101 at Yahoo.com. And you can also reach me at 404-647-425-0 if you want to hit me up.
Again, this is a very quick, quick and dirty, you know, proof of concept for what I'm doing.
What I'd like is to again, you know, have, have these episodes, have them so tagging automated
as much as possible and then have like keyword stuff. So when you can, you can search the full text
or you can search kind of keywords key, key phrases, which would be like a not as deep search
or you can search just based on, you know, automated tags. So there's kind of three levels of
of search there. Now at the end of the day, maybe we don't need any of that. Maybe we just need
the transcription. And that might be good enough for the listeners. It might be even good enough for
people trying to write show notes and stuff like that. We might even be able to use the output,
right, of some of these scripts to help generate show notes for, generate show notes and tags. So,
you know, instead of having to listen to the entire episode, we use machine learning to
pull phrases out, keyword out, we review those keywords and say, okay, well, obviously this
episode is about Gimp, but I don't want the word, you know, banana in there because the whole
episode is about photoshopping or excuse me, gimping a banana, right, using Gimp on a picture of a banana.
So if the speaker uses the, you know, keyword a bunch of times or key phrase and it's not
what the episode's about, right, we can filter that out. So that might be kind of some way we could
use, you know, machine learning way, whatever to help with show notes and tagging. Maybe make it,
you know, halfway there. Maybe, right, this is some other features thinking just off the top of my
head. Maybe the transcribe comes in, the transcribes gets analyzed and it creates key words and tags.
And then those go to what do we call janitors to approve or disprove, right? So instead of, you know,
the janitors haven't listened to every single episode. I mean, as much as I want to hear other
people's voices in my own voice, it can be, it can be kind of hard to go through some of these episodes
that are hours long or 30 minutes long that has, it's on a topic that you're not really passionate about.
So having to mentally listen, right, even out of 2x, haven't to mentally listen and retain that
knowledge. Yeah, you can probably listen to the first 5 minutes and get an idea about what the
episode's about. And maybe we use machine learning for that piece too. Maybe we analyze the first 5 minutes,
pull out the key phrases, those get pushed into a queue for janitors to approve kind of automated
show notes and or automated tagging. And they can say, okay, well, based on the transcription,
yeah, I'll approve these four keywords or these four tags. And these other three are just stupid
and they need to be filtered out. And, you know, we can kind of have a white list and a black list
of keywords and phrases so that, you know, instead of managing and having to like uncheck free
software every time, then you can kind of highlight that and filter it in or out based on, you know,
based on things like that. So just basic filtering, basic, basic stuff like that.
Other than that, I can't really think of anything else off the top of my head.
If you all have any comments, suggestions, ideas, you know, there's, there's the, you know,
10,000 ways to skin this cat. But at the end of the day, I can probably provide, give it enough
time and maybe a little bit of help with some people. I could probably automate tagging.
Which is kind of scary with machine learning. So just keep that in mind. If, and if we were,
you know, we're against the robots and we think that the human touch is important, whatever. But
I'm a big scripter. I'm a bit of automator and I definitely don't want you guys getting burned out.
And, you know, if I can help with manual tagging, I probably should. Instead of doing episodes about,
you know, automating something that doesn't need to be automated. But anyways, hope that helps you
guys out. Feel free to pass off any other ideas or anything or provide comments. I've, I've really
thought about this for several years and finally broke down and said, you know what? I, you know,
I'm, I'm, I've hear so many, so many talking about, you know, tagging and having to do this and
having to do that. Well, if we can, at the very least, transcribe, right? These episodes, then
that makes that can cut down on a lot of the work having to do, having to be done. Anyways,
you all have a good one and peace out.
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