Transcript
00:00:00Speech-to-text is good enough now that transcription usually isn't the problem, but the cleanup still is.
00:00:06You can throw GPT or clod at every messy transcript, or use a 600 million parameter model built exactly for this job.
00:00:14Super Whisper just released S1 Mini. It runs fully local, clean fillers, corrections, numbers, and formatting.
00:00:22Plus, it's small enough to drop straight into your own ASR pipeline.
00:00:26I tested it, and it's time to show you where all this is headed.
00:00:30S1 Mini. It's all about the pipeline, so think of it like this.
00:00:39Your speech goes into whatever transcription system you're already using.
00:00:43Whisper, Whisper CPP, Parakeet, your own ASR service.
00:00:47That gives you raw text.
00:00:50Then, S1 Mini sits directly after it.
00:00:53Raw transcript in, clean text out.
00:00:56It removes those fillers like "um", "uh", it resolves false starts and self-corrections, it formats numbers, dates, and email addresses.
00:01:06You can even control the tone of the output.
00:01:09Then, none of that requires sending your transcript to another API.
00:01:12I think that most of the core value is all this.
00:01:16I'm not replacing a transcription stack.
00:01:18I'm just adding one tiny cleanup layer to all of it.
00:01:21Super Whisper also released S1 Voice and S1 Language, but those are their cloud models.
00:01:27For us, S1 Mini is the interesting one, because this one is the open one.
00:01:32And this one can run completely locally, so let's see if it actually works.
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00:01:41All right, now I'm running this on my Mac M4 Pro, and the first surprise is the size.
00:01:46I don't have to download some giant local model.
00:01:49The quantized version I'm using is around 462 megabytes.
00:01:53I just grabbed it from Hugging Face and ran a curl to install it.
00:01:57And because GGUF versions are available, you can run it with the normal local tooling.
00:02:02So Lama CPP, Olama, LM Studio, it's really your choosing.
00:02:07Whatever already fits your workflow, but here I chose Olama as I already had it going.
00:02:13After I installed the model and made a simple model file on my desktop too, I can serve it
00:02:18up and run it with Olama.
00:02:20Now, keep in mind, this is a transcription cleaner.
00:02:23So let's see how the cleanup here is and how we can integrate that into our workflow.
00:02:28Let me give it something intentionally just bad, all right?
00:02:31Here's the raw transcript.
00:02:33You can see here that I stumbled over a bunch of things.
00:02:36I had, uh, it's broken.
00:02:37All this stuff is really going on.
00:02:39And at the very end, I say email support@betterstack.com.
00:02:43This has basically everything that makes speech to text annoying.
00:02:47It has a filler at the beginning, a correction halfway through.
00:02:50There's spoken numbers.
00:02:52There's just a bunch going on in that email address that is said out loud.
00:02:55All this is where a general LLM could easily do too much.
00:03:00Let's see what S1 mini does instead.
00:03:02Run it through the model and boom, there we go.
00:03:05And those fillers disappear instantly with no lag at all.
00:03:08It understands that I changed my mind partway into it.
00:03:11The email address gets formatted properly and the result reads much more like something I actually
00:03:17meant to type.
00:03:18Sure.
00:03:19Here are a few other real quick ones I can run through.
00:03:22We got one here.
00:03:23Okay.
00:03:24Okay, quick.
00:03:25Another one here.
00:03:26Looking good.
00:03:27And this one here.
00:03:29You can see all those were near instant and you can see the output of all them with a quick
00:03:34cleanup.
00:03:35It doesn't suddenly turn one sentence into just a long rambling email.
00:03:39It leaves most of the meaning alone.
00:03:41It just cleans up the mess around it.
00:03:44And for transcript cleanup, that's exactly what I want.
00:03:47Now, let me work this into a quick flow here because it would be cool to put this in another
00:03:51real layer with some live speech.
00:03:53I have some pre-recorded audio just here on my Mac.
00:03:56It's around 10 seconds.
00:03:57I'll just use MLX Whisper since it's clean to run on my Mac first.
00:04:02We'll transcribe it here with this run.
00:04:05Then when I run this command here, it does the cleanup just as good and outputs it into
00:04:09text.
00:04:10That was taking my real audio file and working with it.
00:04:14Now, because S1 mini probably shouldn't be the main model in your application.
00:04:18We don't really want that.
00:04:19It makes more sense as a tiny layer between two other things.
00:04:23Imagine you're building a completely local dictation setup.
00:04:26Whisper CPP handles the audio.
00:04:29S1 mini handles the cleanup.
00:04:31Then the file text goes straight into your editor, so nothing leaves our machine.
00:04:35That's it.
00:04:36And that's where S1 mini should go.
00:04:38Raw text is coming in.
00:04:40Clean text is going out.
00:04:42And there are a bunch of places where that matters more than it saddles, right?
00:04:46So, I don't know.
00:04:46Take coding agents.
00:04:47Clean the correction before it reaches the agent, and the instruction becomes much clearer.
00:04:52Same thing with voice written Slack messages.
00:04:54Email support tickets.
00:04:56Meeting notes.
00:04:57Anywhere you're speaking the input, but you want the result to look like written text.
00:05:01Every cleanup pass can stay local.
00:05:04There's no extra API call and transcript going somewhere else.
00:05:08And there is the obvious question here.
00:05:10If big models already do this well, why are we even bothering with this tiny one?
00:05:14Sure, yeah.
00:05:15You could absolutely send the transcript to GPT or Claude or a local llama model, and they
00:05:21would probably clean it up just fine.
00:05:23But that's also the problem here.
00:05:25Because all those models can actually do too much.
00:05:28A general LLM can rewrite your transcript, summarize it, expand it.
00:05:32You've all seen this happen before.
00:05:34You know what happens when we give an LLM something like that.
00:05:37It can basically take the text in any direction it wants.
00:05:41S1 mini has a much smaller job.
00:05:43Take messy speech, normalize it.
00:05:46That's really it.
00:05:47So now we're using a 600 million parameter model designed for exactly this kind of transformation.
00:05:53So for this one task, you're potentially getting lower resource usage, lower latency.
00:05:57And if you're doing this at scale, there's no cleanup API bill.
00:06:01It's also worth separating S1 mini from something like local whisper.
00:06:05Whisper solves one problem.
00:06:07Audio becomes text.
00:06:09S1 mini solves the next one.
00:06:12That text becomes something that looks like a human intentionally typed it or wrote it out.
00:06:17So the pipeline is really simple.
00:06:20Speech, raw transcript, clean text.
00:06:22Whisper now has models for both sides of that workflow.
00:06:25But the important part is that you don't need to use their entire stack.
00:06:28You can take S1 mini and drop it into your own.
00:06:31And I think that's why this model is more interesting than another tiny LLM release.
00:06:35But hey, this isn't perfect.
00:06:36There are a few limits we gotta know before dropping it into anything important.
00:06:40Now the good stuff.
00:06:41Well, the good stuff.
00:06:42It's tiny.
00:06:43It's fast.
00:06:43It can run fully locally.
00:06:44So once transcription is done, there's no network round trip just to clean up the text.
00:06:49This is a huge win.
00:06:50This is especially strong on the obvious stuff.
00:06:53Fillers, self-corrections, spoken numbers, basic formatting.
00:06:56But now we get to the first really big limitation.
00:06:59It's English only right now.
00:07:01So if your workflow needs multilingual transcript cleanup, this isn't the tool today.
00:07:05Second, don't mistake its narrow focus for a weakness.
00:07:08You're not downloading S1 mini to replace Claude or anything else.
00:07:12If you want reasoning, summarization, or a general local agent, use Claude.
00:07:17Use a general model.
00:07:18S1 mini only cleans transcripts.
00:07:21That's the job.
00:07:22And there's another distinction that's easy to make.
00:07:24S1 mini is not S1 voice.
00:07:26S1 voice is Super Whisper's cloud speech recognition model.
00:07:30That's a separate part of the stack from what I'm testing here.
00:07:34The whole S1 family is also very new.
00:07:36It dropped on August 19th, so I would still treat it as early software.
00:07:41The model itself can run standalone, so the simple question becomes pretty simple.
00:07:46Who should actually use this?
00:07:48If you already run local speech-to-text, S1 mini makes a lot of sense, especially if you're
00:07:53using Whisper CPP, Parakeet, or your own ASR system, and you've been wondering what to
00:07:58do with the transcript afterward.
00:08:00Instead of sending everything to a full general-purpose LLM, which a lot of us already do, you can put
00:08:06this small model right after transcription and let it handle the cleanup.
00:08:10If privacy matters, this gets more useful.
00:08:12It's local.
00:08:13Anything worth sending the transcript to another cloud model just for formatting feels unnecessary.
00:08:18If you're processing a lot of speech, that makes sense.
00:08:20All these little cleanups, they're API calls, so we don't actually need those if we're using
00:08:24S1 mini.
00:08:25I'll put the S1 mini hugging face page in the description if you want to check it out yourself.
00:08:30If you enjoy coding tips and tricks like this, be sure to subscribe to the BetterStack channel.
00:08:33We'll see you in another video.
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