Anyone know a good Windows app that's just a window that transcribes - and translates - whatever goes through your output device, and not the microphone like most apps do?
Looks very cool. One thing I have been looking for, which this doesn't seem to cover (at least I didn't see any mention of IPA in the model documentation), is a way to transcribe unknown languages phonetically, using the International Phonetic Alphabet to spell them (sound-based spelling rather than meaning-based spelling). I know several linguists doing research on minority languages (fewer than 10,000 speakers in some cases), which are small enough that they will never have enough effort made towards training language-specific models in that language.
Are there models I'm not aware of that are trained for this task? Taking audio in an unknown language, and rather than identifying the language, just transcribing the sounds to IPA? That would not be useful to most people, but it would be a Godsend to many, many linguists working with minority languages around the world.
I would love such a model. My wife's family is Iu Mien which is a sub group of the Dao/Yao Chinese ethnic minority. Mien is its own language but most speakers are essentially illiterate. I'm good with language but there simply isn't a course or any books for learning the language. Not much in writing to begin with given the high illiteracy rate. I would love to build a translation system - project Hail Mary style :)
We take a lot of shortcuts when speaking, it's actually much harder to transcribe phonemes than to transcribe words, even when aware of the language being spoken. Some models have been trained for the task (e.g. look at https://huggingface.co/spaces/KoelLabs/IPA-Transcription-EN ), but the error rate is really high.
There are, broadly, two kinds of audio recordings that linguists want to transcribe. One is native speakers telling traditional stories, where they're speaking naturally and taking the natural shortcuts (such as "wanna" and "gonna" in English). The other is native speakers reading words (or short example sentences) very carefully and distinctly, so that the linguist can listen to the recording over and over to learn how to pronounce the word right. In those recording, they'll say "want to" and "going to" rather than "wanna" and "gonna".
Thanks for the pointer; I'll check out that model and see if it handles the "slowly and carefully" type of recording better than the "natural speaking" type. (And depending on what kinds of errors the model makes, even the recordings where it makes errors can prove useful: for example, a linguist studying regional variations in speech would want the model to produce the IPA for "gonna" rather than "going to").
Largely this is out of scope for the library, mainly because I’m not aware of many models supporting this. but if there are models which support this would be happy to support
Congrats on shipping this. I love handy on my Mac, my phone for STT in situations where it’s not possible/poor performance of the native
Model for STT(e.g apple’s thing is not upto scruff, like mistranslating words corresponding to a domain).
Noob question: How do you think about funding from a foundation(i have no clue if you need it or not, I do hope you have a way to get paid one way or another because handy is amazing) for maintenance of this? if you did or were going to get paid by asking for maintaining such a project what might be the kind of organizations you would look for to get supported and how would you do it?
Thanks! What an excellent question, I’m not sure I have a good answer. I kind of became an open source maintainer by accident as Handy became popular
Certainly I am very lucky that quite a few people donate to Handy, and also some people and organizations who sponsor the work I do
To be honest I just love contributing to open source and wish to continue to do so. So anyone who supports this is good to me. Organizations which believe in OSS and push it forward are typically most aligned with me
Of course you can always email me ([email protected]) and we can discuss in more detail
OS-native dictation on iOS requires uploading your address book to Apple on every request, even if you don’t use iCloud. I unfortunately have to leave it disabled for this reason.
Of all the corporations to send data to, I would trust Apple the most. Do you have a trust issue with them, or do you just not want to send data in general?
For anyone looking to build on top of this. I have tried a few different STT systems, and they accurately capture what I am saying. Unfortunately, they don't support the reasonable workflow
I want to open an office document, for example, and start talking. And I want the software to continuously type what I am saying at the cursor with minimal latency. The continuous part is crucial. Many software will paste whatever I said after I have stopped recording, but that is not useful.
Totally understandable, but I’ve found that software that transcribes everything after I finish recording actually works better for me. I’ve tried both kinds, and systems that continuously type what I’m saying distract me from completing my thought. I end up reading what’s being typed and noticing transcription mistakes instead of focusing on what I’m trying to say.
I often prefer to dictate everything in my head about a particular thing for 5–10 minutes and then go through it afterward. I find that much more useful because it doesn’t break my thought process the way continuous transcription does.
A while ago, I auditioned about 10 different STT apps on my Mac, with this realtime/streaming transcription as a goal. I failed to find that feature in an app I was happy with, but settled on Handy as the best option otherwise. So if Handy adds this, it will be perfect!
I’m on a train right now but off the top of my head the audio pipeline may have to be modified slightly to emit partial text segments as they come in from the transcription engine. And then calling the appropriate paste method the user has in their settings.
It may be easier than expected in some way since we already emit events for the live overlay, so it could be as small as a function call, but I don’t know the code path well enough from memory and what complexities it has. Probably with the Tauri context and a bit of other mess we have as this bit of code has gone through a lot of pain
It may be interesting to have it immediately insert the words, even if they are wrong, and when a sentence is finished, replace what has been written with the final corrected sentence.
Google released this awkwardly named app called edge eloquent recently that does exactly that.
In fact, it cleans up the entire paragraph that you just said, and even if you have meandering thoughts, it cleans those up too.
Actually, this above statement was fully dictated with iOS and it added all the punctuation automatically, so I think that iOS is also doing some of this natively. In fact, I’m on the iOS 27 beta and it seems to be doing an even better job of correcting itself and correcting earlier words and adding punctuation too.
I tried this on my Mac soon after launch and it was consuming a significant amount of processor cycles even just sitting idle in the menu bar. (From memory, ~20% of an M1 Max.)
It may have been an early issue but with no obvious way to interact and report the issue and, eh, Google’s general attitude around customer satisfaction, I just gave up and deleted it again.
But this is still possible to do if you track the whole run of text. You could replace all of it each time so it LOOKS like it’s streaming but earlier words also change. I’m hoping the streaming models do this eventually.
I believe the built-in iOS dictation already does this.
More accuracy. Like others have said, homonyms (their, they're, there) is easier to determine once you have more context. So then you may need to go back a couple words and update them.
Same with punctuation, you could determine that a comma belonged in a certain place once you have enough words.
In iOS this means you can edit the text as it’s being transcribed. For example, I want to dictate a todo list and after each item I can hit enter to go to the next line.
I suspect the hard bit is that it sometimes needs to back up and redo, and that's an interface they haven't got figured out. I'm fairly sure I remember Dragon Naturally Speaking doing it in Word years ago though, so the interfaces should be there.
Model should be able to understand where logical sentence ends, to stop buffering, and optionally rewrite some of the test that has already been output.
> The continuous part is crucial. Many software will paste whatever I said after I have stopped recording, but that is not useful.
It really depends on how one uses transcription.
For example, I really value being able to open different windows, and look at graphs, or scroll some data while I'm dictating, because it can help me with providing some support information for what I'm saying.
Some apps can even take into account things you copy or look at as part of the transcription's context to improve the results [0].
Apple Dictation does this, or something similar, in my experience. Some apps (e.g. terminals in my experience) buffer the entire transcript but in most apps it's identical to typing as you speak. Have you tried it?
What good timing to spot this. I've been reading more and more people talk about bringing TTS into their prompting toolkit and wanted to give that a try. The idea of rambling brain dump into a doc -> edit pass -> send to the robot loop sounds appealing.
Is there something but for transcribing what you watch like videos and not your microphone? Samsung has this in my phone and it's useful for language learning. (Thought is not that accurate)
The post makes it seem like ONNX is CPU only. I've used ONNX runtime to run models on Nvidia GPUs. The runtime can even dispatch to TensorRT. I'm not sure what the performance is on Apple hardware so maybe that was the motivation for moving away from ONNX.
TensorRT and CUDA is effectively the same speed as CPU for the speech to text models I was testing via ONNX at a huge binary bloat penalty. WGPU is hard to ship and also equivalent speed or slower. This may not be the case for LLM or other models but the runtimes did not seem well supported for what I needed to do. ONNX is incredibly well optimized for CPU, best in class even, but the other execution providers at least for STT seemed lacking.
I did this investigation before creating transcribe.cpp it would have been much more convenient and save me literal months of work. Happy to share the repo and binaries produced as well, but it was mostly throw away work to profile how to ship accelerated ONNX in Handy.
This is an incredible contribution to the community and it's just... one guy? I kept reading expecting a Series A funding announcement at the bottom.
It's a nice reminder: You can use AI to slop cannon at maximum speed, or you can use it to scale your ambitions and build something more rigorous and lasting than ever before.
I'd build Transcribe.cpp into the apps I maintain, but I feel like this functionality should (generally) be integrated into the OS or "everywhere" via an app like Handy.
Hey, yep author and maintainer here! Certainly sponsors help and the wonderful community who donates to Handy as well! Mozilla AI was very helpful in getting this work off the ground. It was a pipe dream for me to build for Handy and they helped to sponsor me so I could make time to take this project seriously and get a v0.1.0 release out the door
I agree this should be everywhere and I hope to distribute libtranscribe some day properly so it is more a system library! It will take time to stabilize but I think we can get there
Really cool that every model is actually tested for accuracy instead of just claiming it works, I think alot of 'we support everything' tools skip that step. How are you checking accuracy for models that don't have an obvious "official" version to compare against?
Every model with open weights has some code which can be used to inference it. So we download the published weights and run against inference library they suggest, be it transformers, Nemo, etc
Nice. I did transcriptions on a casual project before that went through something like this. Transcribing videos or audio files with Whisper? Very common. But having to swap it out with Qwen3 or a different family of ASR models? Oops, not as straightforward. For Qwen for example you gotta deal with the forced aligner or it won't be good as subtitles, and then gotta deal with some requirements and considerations if you want to make use of MLX on a Mac or something.
Will definitely check this out since it sounds like it eases through the pain of dealing with these.
Nice. Here's the Python one: https://github.com/handy-computer/transcribe.cpp/tree/main/b... - looks like it's not yet available as a binary wheel on PyPI with the dependency included (the library on PyPI right now uses ctypes to call a separately installed library) but that's planned for a future release.
Amazing, i've been looking for something like this and ended up doing transcription + diarization on a local server for now. Are you looking for contributions? Have you tried this one for diarization - https://huggingface.co/pyannote/speaker-diarization-communit... - it performed much better than Sortformer for me.
Contributions are always welcome! There’s a WIP diarization PR rn, and after it’s merged would love to have support if it fits well into the interface. And if not would love to figure out a good interface for it
Yeah, diarization is the real feature these days. STT needs uniformization, but quality of diarization is what is setting personal solutions apart in this field.
Yeah I’m working on it, Linux is a big pain point especially Wayland
Once things are more or less ironed out on MacOS and Windows a lot of attention will be turned towards Linux
I know a lot of Linux PRs are open it just takes me so long to get around and test them. And often multiple different implementations trying to fix similar issues which is a lot of overhead sometimes
Is there any way people can help? From your last sentence, it sounds like another PR isn't it and the opposite might be needed. But would love to contribute with testing if helpful. I'm regularly jumping between XFCE, KDE, GNOME, Niri, etc..
Testers by far as the most needed thing, I do maintain a list of per platform people who help to test so if you drop a GitHub username (or email me) I will add you to the list and ping for help
Basically the biggest blocker is me being the sole maintainer and reviewer at the moment and it just ends up taking a lot of time for the scale of the project. Which is why it moves slow and features typically are much slower than someone can vibe code. I know each added feature inevitably has bugs so I try to be careful with them.
But also Linux has historically been a minefield, fixing something for someone breaks for someone else so yeah testers really needed. Or anyone with deeper Linux DE knowledge than I have. I’m much more accustomed to server based Linux distros
I have a personal fork of hyprvoice[0] which I use almost everywhere now (w/ the big cohere-transcribe running on a local vLLM instance). It does a similar thing, but that's not why I'm mentioning it; I think it's worth looking at because it's a clean reference for the few elegant ways you can implement text injection in modern Linux (wayland).
It supports ydotool[1], wtype[2] and "clipboard fallback with clipboard restore". The first two you can probably think of as AHK equivalents - they wire in at the input layer and inject keystrokes when injecting text. wtype is wayland-only and a bit less invasive, ydotool supports non-wayland also apparently, but I haven't tried it. Neither approach provides 'instant text' - you have to watch the text get typed out, and you don't touch your keyboard while it's happening; the clipboard implementation is fallback for a reason as it's the least reliable. The first two work 'well enough' though, and are fairly tunable.
The other thing hyprvoice does in probably the most linux-friendly and universal way is the 'hotkey handling'. The server creates a socket in /tmp that the cli can then ping when the user triggers the start/stop/cancel, and they do this by binding whatever their DE's keyboard shortcut mapping mechanism is to trigger `hyprvoice toggle` as a background shell command. This works extremely well and is much cheaper than you'd intuitively think coming from Windows. This way you don't have to interface with DE-specific global keyboard listeners etc, but leave that to the WM (that's not to say that your installer couldn't prompt the user to configure the keyboard shortcut for them with their detected WM, you just wouldn't do it in the software itself).
I haven't actually looked at your project in too much depth yet as I have a solution for this already, so apologies if none of the above is news to you. Hope it helps though - happy to poke around and contribute something if the gap's still there.
Yep, but I am in the process of also porting NVIDIAs Sortformer for multi speaker diarization as well :)
I’m not sure how many specific models will be supported as the library is more focused on transcription specifically. But the models which support diarization natively must be supported I think. And parakeet multitalker was the primary driving force for this change
How close do you aim for when it comes to drop-in vs whisper.cpp? Are timestamps per word and character something aimed for? How about multi-lingual transcription or hallucination suppression?
The github page doesn't seem to go into depth on these orthogonal topics. May have missed it.
Eventually I would like to be more fully drop in compatible, right now some feature support is a bit sparse. And whisper has so much work done to it over the years so it’s hard to support every possible thing. Right now it’s a more bog standard implementation than anything special. Right now stabilizing the core header is probably among the primary goal, but if people want to contribute model specific things im happy to review test and pull in. Whisper is a good case for this as there is a header extension already so it’s easier
> I think as we look forward to the future, more inference will start happening locally for one reason or the other. This brings the distribution story front and center. In order to have more applications running inference locally, we need to make running inference easier.
This makes these projects so much more trustworthy and easier to approach:
> Were any of the words here written using AI? Nope. They came from my mouth or my fingers.
>This makes these projects so much more trustworthy and easier to approach:
>> Were any of the words here written using AI? Nope. They came from my mouth or my fingers.
I have to push back on this a bit, as I believe (quite strongly) that we're shaped by the tools we use; text-to-speech LLMs are still LLMs, and generally their mistakes are shaped by the expectations inherent in their training. This, in turn, shapes the words that appear on the screen. For those who regularly use them, you then learn which word sequences are likely to be accurately transcribed, and this definitively becomes part of your thinking process. Over time, the LLM becomes tangled into your thinking; the use of AI, even in this way, very much can and often does shape the resulting words.
Isn't this like saying "my words are not really my own when I speak to my family, because I know my father is a non-native English speaker and hard of hearing so I try to use words which are well enunciated and are few in syllable count"?
You can take it one step further! As Tyutchev wrote, "A thought once uttered is a lie." [1] Speech is a projection of a thought, and a lossy one. So no matter who is the listener, the speaking/writing does affect the thinking. Though comment on LLM transcribing is spot on.
Is transcription a form of _inference_ though? I mean I see the word being thrown around and I understand what it means (or at least I think I do) in context of LLMs doing the thing that they do -- intelligently predict the next token, but do speech-to-text models do that?
three separate people in this thread independently remembered Dragon NaturallySpeaking and I think that is the funniest possible review of the state of speech recognition in 2026
What's the best local TTS model right now? I'm running parakeet on a mac which transcribes all my uh's and aahs. I'm running whisper on linux/cuda and I by far prefer that one over parakeet.
Not in the library itself, it’s pure inference. Some models have this trained out of them anyhow. Otherwise this is a post processing task which is not really inference
After seeing so many *subscription based* transcription apps all wrapping *open source models*, finding Handy was a real delight and I'm happy to see the author keep on building!
Nice - I'm definitely going to take a look at this. I've built my own cross-platform (Mac/Win/Linux) live captioning app on top of Nemotron, and it works well but dealing with ONNX is kind of annoying. With this having Rust support (I built it on Rust/Tauri) it should be a pretty solid candidate; I'll have to see if I can find a Silero VAD implementation that doesn't depend on ONNX, or maybe I'll see if the clankers can migrate it for me.
Congrats on delivering good value to the people. I have used transcribe.cpp a few weeks ago to do near realtime offline stt on a 10 year old phone, writing simple adhoc app for my use case, it's crazy what is happening right now.
Handy is an amazing cross-platform app for dictation from the author. There are other awesome open-source dictation tools as well like native macOS ones. You do not need SaaS subscription in this day and age for transcription.
I maintain this list of all the best open-source ones in this awesome-style GitHub repo. People looking for open-source dictation tools, hope you find something that works for you here:
If you're talking about translated text, then that should be super easy. Most of these dictation tool support post-processing with LLM to remove filler words, fix punctuation, etc. I'd imagine you can change the system prompt for the post-processing step to do the translation instead, and you'd get translated text.
I assume this is going to make maintaining SubtitleEdit a lot easier from now on, too: https://github.com/SubtitleEdit/subtitleedit/.
Anyone know a good Windows app that's just a window that transcribes - and translates - whatever goes through your output device, and not the microphone like most apps do?
Are there models I'm not aware of that are trained for this task? Taking audio in an unknown language, and rather than identifying the language, just transcribing the sounds to IPA? That would not be useful to most people, but it would be a Godsend to many, many linguists working with minority languages around the world.
Thanks for the pointer; I'll check out that model and see if it handles the "slowly and carefully" type of recording better than the "natural speaking" type. (And depending on what kinds of errors the model makes, even the recordings where it makes errors can prove useful: for example, a linguist studying regional variations in speech would want the model to produce the IPA for "gonna" rather than "going to").
Noob question: How do you think about funding from a foundation(i have no clue if you need it or not, I do hope you have a way to get paid one way or another because handy is amazing) for maintenance of this? if you did or were going to get paid by asking for maintaining such a project what might be the kind of organizations you would look for to get supported and how would you do it?
Certainly I am very lucky that quite a few people donate to Handy, and also some people and organizations who sponsor the work I do
To be honest I just love contributing to open source and wish to continue to do so. So anyone who supports this is good to me. Organizations which believe in OSS and push it forward are typically most aligned with me
Of course you can always email me ([email protected]) and we can discuss in more detail
> Dictation sends information like your voice input, contacts, and location to Apple when necessary for processing your requests.
I want to open an office document, for example, and start talking. And I want the software to continuously type what I am saying at the cursor with minimal latency. The continuous part is crucial. Many software will paste whatever I said after I have stopped recording, but that is not useful.
I often prefer to dictate everything in my head about a particular thing for 5–10 minutes and then go through it afterward. I find that much more useful because it doesn’t break my thought process the way continuous transcription does.
I’m planning on having it as a first class feature of the app too just too many other issues to work on first
A while ago, I auditioned about 10 different STT apps on my Mac, with this realtime/streaming transcription as a goal. I failed to find that feature in an app I was happy with, but settled on Handy as the best option otherwise. So if Handy adds this, it will be perfect!
It may be easier than expected in some way since we already emit events for the live overlay, so it could be as small as a function call, but I don’t know the code path well enough from memory and what complexities it has. Probably with the Tauri context and a bit of other mess we have as this bit of code has gone through a lot of pain
In fact, it cleans up the entire paragraph that you just said, and even if you have meandering thoughts, it cleans those up too.
Actually, this above statement was fully dictated with iOS and it added all the punctuation automatically, so I think that iOS is also doing some of this natively. In fact, I’m on the iOS 27 beta and it seems to be doing an even better job of correcting itself and correcting earlier words and adding punctuation too.
It may have been an early issue but with no obvious way to interact and report the issue and, eh, Google’s general attitude around customer satisfaction, I just gave up and deleted it again.
I believe the built-in iOS dictation already does this.
Same with punctuation, you could determine that a comma belonged in a certain place once you have enough words.
So in general this definitely works. Handy is just missing the feature to insert these streamed words into the app where the cursor is.
It really depends on how one uses transcription.
For example, I really value being able to open different windows, and look at graphs, or scroll some data while I'm dictating, because it can help me with providing some support information for what I'm saying.
Some apps can even take into account things you copy or look at as part of the transcription's context to improve the results [0].
[0]: https://superwhisper.com/docs/common-issues/context#types-of...
However the accuracy of the real time models is poor, so I did a second pass with a higher accuracy model before committing the text.
It looks like the rust bindings have streaming examples so hopefully there is a nice solution here.
I did this investigation before creating transcribe.cpp it would have been much more convenient and save me literal months of work. Happy to share the repo and binaries produced as well, but it was mostly throw away work to profile how to ship accelerated ONNX in Handy.
It's a nice reminder: You can use AI to slop cannon at maximum speed, or you can use it to scale your ambitions and build something more rigorous and lasting than ever before.
I'd build Transcribe.cpp into the apps I maintain, but I feel like this functionality should (generally) be integrated into the OS or "everywhere" via an app like Handy.
I agree this should be everywhere and I hope to distribute libtranscribe some day properly so it is more a system library! It will take time to stabilize but I think we can get there
Will definitely check this out since it sounds like it eases through the pain of dealing with these.
Nice. Here's the Python one: https://github.com/handy-computer/transcribe.cpp/tree/main/b... - looks like it's not yet available as a binary wheel on PyPI with the dependency included (the library on PyPI right now uses ctypes to call a separately installed library) but that's planned for a future release.
If there’s any issues or improvements on the bindings I would love help to make the DX the best it can be
Once things are more or less ironed out on MacOS and Windows a lot of attention will be turned towards Linux
I know a lot of Linux PRs are open it just takes me so long to get around and test them. And often multiple different implementations trying to fix similar issues which is a lot of overhead sometimes
Is there any way people can help? From your last sentence, it sounds like another PR isn't it and the opposite might be needed. But would love to contribute with testing if helpful. I'm regularly jumping between XFCE, KDE, GNOME, Niri, etc..
Basically the biggest blocker is me being the sole maintainer and reviewer at the moment and it just ends up taking a lot of time for the scale of the project. Which is why it moves slow and features typically are much slower than someone can vibe code. I know each added feature inevitably has bugs so I try to be careful with them.
But also Linux has historically been a minefield, fixing something for someone breaks for someone else so yeah testers really needed. Or anyone with deeper Linux DE knowledge than I have. I’m much more accustomed to server based Linux distros
It supports ydotool[1], wtype[2] and "clipboard fallback with clipboard restore". The first two you can probably think of as AHK equivalents - they wire in at the input layer and inject keystrokes when injecting text. wtype is wayland-only and a bit less invasive, ydotool supports non-wayland also apparently, but I haven't tried it. Neither approach provides 'instant text' - you have to watch the text get typed out, and you don't touch your keyboard while it's happening; the clipboard implementation is fallback for a reason as it's the least reliable. The first two work 'well enough' though, and are fairly tunable.
The other thing hyprvoice does in probably the most linux-friendly and universal way is the 'hotkey handling'. The server creates a socket in /tmp that the cli can then ping when the user triggers the start/stop/cancel, and they do this by binding whatever their DE's keyboard shortcut mapping mechanism is to trigger `hyprvoice toggle` as a background shell command. This works extremely well and is much cheaper than you'd intuitively think coming from Windows. This way you don't have to interface with DE-specific global keyboard listeners etc, but leave that to the WM (that's not to say that your installer couldn't prompt the user to configure the keyboard shortcut for them with their detected WM, you just wouldn't do it in the software itself).
I haven't actually looked at your project in too much depth yet as I have a solution for this already, so apologies if none of the above is news to you. Hope it helps though - happy to poke around and contribute something if the gap's still there.
[0]: https://github.com/leonardotrapani/hyprvoice [1]: https://github.com/ReimuNotMoe/ydotool [2]: https://github.com/atx/wtype
Looks like it's using IBM's Granite-Speech-4.1-2B-Plus https://huggingface.co/ibm-granite/granite-speech-4.1-2b-plu... and/or MOSS-Transcribe-Diarize https://huggingface.co/OpenMOSS-Team/MOSS-Transcribe-Diarize
I’m not sure how many specific models will be supported as the library is more focused on transcription specifically. But the models which support diarization natively must be supported I think. And parakeet multitalker was the primary driving force for this change
The github page doesn't seem to go into depth on these orthogonal topics. May have missed it.
> I think as we look forward to the future, more inference will start happening locally for one reason or the other. This brings the distribution story front and center. In order to have more applications running inference locally, we need to make running inference easier.
This makes these projects so much more trustworthy and easier to approach:
> Were any of the words here written using AI? Nope. They came from my mouth or my fingers.
>> Were any of the words here written using AI? Nope. They came from my mouth or my fingers.
I have to push back on this a bit, as I believe (quite strongly) that we're shaped by the tools we use; text-to-speech LLMs are still LLMs, and generally their mistakes are shaped by the expectations inherent in their training. This, in turn, shapes the words that appear on the screen. For those who regularly use them, you then learn which word sequences are likely to be accurately transcribed, and this definitively becomes part of your thinking process. Over time, the LLM becomes tangled into your thinking; the use of AI, even in this way, very much can and often does shape the resulting words.
1. https://www.poetryloverspage.com/poets/tyutchev/silentium/li...
tysm for shipping this, keep up the great work OP
The M4 max has probably 10x the compute and memory bandwidth hahaha
But the answer largely depends on you, the languages you speak, and personal preference. Whisper is still excellent and supported in transcribe.cpp
Cohere Transcribe is also excellent, but many of the new models are as well
You should be able to fix this by playing with the mic speech floor. It happens when to much ambient stuff slurps in.
It's actually gaslighting you, you don't say that many ums and ahs ;)
After seeing so many *subscription based* transcription apps all wrapping *open source models*, finding Handy was a real delight and I'm happy to see the author keep on building!
Nemotron Streaming
Parakeet Unified
Voxtral Mini Realtime
If something you want is not supported, open an issue on transcribe.cpp!
I maintain this list of all the best open-source ones in this awesome-style GitHub repo. People looking for open-source dictation tools, hope you find something that works for you here:
https://github.com/primaprashant/awesome-voice-typing