Show HN: OWhisper – Ollama for realtime speech-to-text
docs.hyprnote.comHello everyone. This is Yujong from the Hyprnote team (https://github.com/fastrepl/hyprnote).
We built OWhisper for 2 reasons: (Also outlined in https://docs.hyprnote.com/owhisper/what-is-this)
(1). While working with on-device, realtime speech-to-text, we found there isn't tooling that exists to download / run the model in a practical way.
(2). Also, we got frequent requests to provide a way to plug in custom STT endpoints to the Hyprnote desktop app, just like doing it with OpenAI-compatible LLM endpoints.
The (2) part is still kind of WIP, but we spent some time writing docs so you'll get a good idea of what it will look like if you skim through them.
For (1) - You can try it now. (https://docs.hyprnote.com/owhisper/cli/get-started)
bash
brew tap fastrepl/hyprnote && brew install owhisper
owhisper pull whisper-cpp-base-q8-en
owhisper run whisper-cpp-base-q8-en
We're here and looking forward to your comments!
Ok, cool! I was actually one of the people on the hyprnote HN thread asking for a headless mode!
I was actually integrating some whisper tools yesterday. I was wondering if there was a way to get a streaming response, and was thinking it'd be nice if you can.
I'm on linux, so don't think I can test out owhisper right now, but is that a thing that's possible?
Also, it looks like the `owhisper run` command gives it's output as a tui. Is there an option for a plain text response so that we can just pipe it to other programs? (maybe just `kill`/`CTRL+C` to stop the recording and finalize the words).
Same question for streaming, is there a way to get a streaming text output from owhisper? (it looks like you said you create a deepgram compatible api, I had a quick look at the api docs, but I don't know how easy it is to hook into it and get some nice streaming text while speaking).
Oh yeah, and diarisation (available with a flag?) would be awesome, one of the things that's missing from most of the easiest to run things I can find.
> I'm on linux
I didn't tested on Linux yet, but we have linux build: http://owhisper.hyprnote.com/download/latest/linux-x86_64
> also, it looks like the `owhisper run` command gives it's output as a tui. Is there an option for a plain tex
`owhisper run` is more like way to quickly trying it out. But I think piping is definitely something that should work.
> Same question for streaming, is there a way to get a streaming text output from owhisper?
You can use Deepgram client to talk to `owhisper serve`. (https://docs.hyprnote.com/owhisper/deepgram-compatibility) So best resource might be Deepgram client SDK docs.
> diarisation
yeah on the roadmap
Nice stuff, had a quick test on linux and it works (built directly, I didn't check out the brew). I ran into a small issue with moonshine and opened an issue on github.
Great work on this! excited to keep an eye on things.
Also had a quick play too. The TUI is garbled thanks to some stderr messages which can just be dev/null'd. I don't seem to be able to interact with the transcripts with the arrow or jk keys.
Overall though, it's fast and really impressive. Can't wait for it to progress.
Oh wait, maybe you do support linux for owhisper: https://github.com/fastrepl/homebrew-hyprnote/blob/main/Form...
Can you help me out to find where the code you've built is? I can see the folder in github[0], but I can't see the code for the cli for instance? unless I'm blind.
[0] https://github.com/fastrepl/hyprnote/tree/main/owhisper
This is CLI entry point:
https://github.com/fastrepl/hyprnote/blob/8bc7a5eeae0fe58625...
Please find a way to add speaker diarization, with a way to remember the speakers. You can do it with pyannote, and get a vector embedding of each speaker that can be compared between audio samples, but that’s a year old now so I’m sure there’s better options now!
yeah that is on the roadmap!
I’ve done something similar recently, using speaker diarization to handle situations where two or more people share a laptop on a recorded call.
Ultimately, I chose a cloud-based GPU setup, as the highest-performing diarization models required a GPU to process properly. Happy to share more if you’re going that route.
What model did you use for diarization?
It seems to use https://api.deepgram.com (and other web endpoints) and apparently needs an API key, so it's not actually local. Why is it being compared to ollama, which does run fully locally?
It can run Whisper and Moonshine models locally, while also allowing the use of other API providers. Read the docs - or at least this post.
I would want such information accessible without having to go hunt for it. You could improve your presentation by interposing fewer clicks between a reader and the thing they want to know.
The information is readily available in the open-your-eyes section.
> I would want such information accessible without having to go hunt for it.
Where exactly, if not in the FM?
Question for folks who work a lot with STT models - What is your favorite model that supports word-level timestamps, has good dysfluency detection (whisper isn't great), and is also supported by transformers.js?
A few months ago I had to work around a problem like this and the best out there was WhisperX. Not sure about transformer.js support.
Link to the repo - https://github.com/m-bain/whisperX
Thank you for taking the time to build something and share it. However what is the advantage of using this over whisper.cpp stream that can also do real time conversion?
https://github.com/ggml-org/whisper.cpp/tree/master/examples...
Its lot more than that.
- It supports other models like moonshine.
- It also works as proxy for cloud model providers.
- It can expose local models as Deepgram compatible api server
Thank you. Having it to operate a proxy server that other apps can connect to is really useful.
Wait, this is cool.
I just spent last week researching the options (especially for my M1!) and was left wishing for a standard, full-service (live) transcription server for Whisper like OLlama has been for LLMs.
I’m excited to try this out and see your API (there seems to be a standard vaccuum here due to openai not having a real time transcription service, which I find to be a bummer)!
Edit: They seem to emulate the Deepgram API (https://developers.deepgram.com/reference/speech-to-text-api...), which seems like a solid choice. I’d definitely like to see a standard emerging here.
Correct. About the deepgram-compatibility: https://docs.hyprnote.com/owhisper/deepgram-compatibility
Let me know how it goes!
I haven’t had the time to properly play around with it yet, but digging into the available meta-info reveals that ... there’s not a lot of it.
When I find the time to set it up I’d like to contribute to the documentation to answer the questions I had, but I couldn’t even find information on how to do that (no docs folder in the repo contribution.md, which the AI assistant also points me towards, doesn’t contain information about adding to the docs).
In general I find it a bit distracting that the OWhisper code is inside of the hyprnote repository. For discoverability and “real project” purposes I find that it would probably deserve its own.
Really neat work! I’ve been experimenting with something similar running a local Whisper model for quick transcriptions, then organizing the notes in a tabbed interface so I can keep different topics separate without switching windows. Vertical tabs have been surprisingly nice for keeping ongoing transcription sessions alongside reference material (I use beavergrow.com for this, but anything with a good tab system would work).
kind of self-plug, but you might find https://github.com/fastrepl/hyprnote/blob/main/README.md interesting.
EDIT: typo
Does it do speaker diarization? That's the one thing that I wish Whisper did out of the box. (I know WhisperX exists, but I haven't had a chance to try it yet.)
EDIT: Ah, I see this was already answered.
For people on osx looking for a no fuss open source pure whisper based local transcription to any input field in the OS you should also try OpenSuperWhisper (can easily be installed with brew)
ah, i use superwhisper (it's great!), I didn't realize there was an open-source version
Very cool. I was reading through the various threads here. I am working on adding stt and tts to an AI DungeonMaster. Just a personal fun project, am working on the adventure part of it now. This will come in handy. I had dungeon navigation via commands working but started over and left it at the point where I am ready to merge the navigation back in again once I was happy with a slimmer version with one file. It will be fun to be able to talk to the DM and have it respond with voice and actions. The diarization will be very helpful if I can create a stream where it can hear all of us conversing at once. But baby steps. Still working on getting the whole campaign working after I get characters created and put in a party :)
I scratched a similar itch and found local LLMs plus Whisper worked really well to listen in and "DJ" a soundtrack while playing tabletop RPGs with a group. If you want to check it out: https://github.com/sean-public/conductor
Very neat project! Congratulations to the founders. I was wondering why there is no one working on such a tool.
But I was hoping couple of features would be supported: 1. Multilingual support. It seems like even if I use a multilingual model like whisper-cpp-large-turbo-q8, the application seems to assume I am speaking English. 2. Translate feature. Probably already supported but I didnt see the option.
I’d like to use this to transcribe meeting minutes with multiple people. How could this program work for that use case?
If your use-case is meeting, https://github.com/fastrepl/hyprnote is for you. OWhisper is more like a headless version of it.
Can you describe how it pick different voices? Does it need separate audio channels, or does it recognize different voices on the same audio input?
It separate mic/speaker as 2 channel. So you can reliably get "what you said" vs "what you heard".
For splitting speaker within channel, we need AI model to do that. It is not implemented yet, but I think we'll be in good shape somewhere in September.
Also we have transcript editor that you can easily split segment, assign speakers.
If you want to transcribe meeting notes, whisper isn't the best tool because it doesn't separate the transcribe by speakers. There are some other tools that do that, but I'm not sure what the best local option is. I've used Google's cloud STT with the diarization option and manually renamed "Speaker N" after the fact.
LOL, and just only yesterday I was looking for a tool like that :D
Though, with a twist that it would transcribe it with IPA :)
I’m looking for something that is aware of what is being discussed realtime, so if I zone out for a few minutes, I can ask it what I missed or to clarify something. Can this do that? If not, anybody know of something that can?
Why not use a LLM with the speech to text output?
Zoom does it quite ok
Given how sentiments towards ollama has become, I'm not sure this is a clever marketing line :D
I have not heard about this. Can you please provide more context?
I wasn't 100% serious, but this should give you some information: https://news.ycombinator.com/item?id=44867238
Happy to answer any questions!
These are list of local models it supports:
- whisper-cpp-base-q8
- whisper-cpp-base-q8-en
- whisper-cpp-tiny-q8
- whisper-cpp-tiny-q8-en
- whisper-cpp-small-q8
- whisper-cpp-small-q8-en
- whisper-cpp-large-turbo-q8
- moonshine-onnx-tiny
- moonshine-onnx-tiny-q4
- moonshine-onnx-tiny-q8
- moonshine-onnx-base
- moonshine-onnx-base-q4
- moonshine-onnx-base-q8
I thought whisper and others took large chunks (20-30 seconds) of speech, or a complete wave file as input. How do you get real-time transcription? What size chunks do you feed it?
To me, STT should take a continuous audio stream and output a continuous text stream.
I use VAD to chunk audio.
Whisper and Moonshine both works in a chunk, but for moonshine:
> Moonshine's compute requirements scale with the length of input audio. This means that shorter input audio is processed faster, unlike existing Whisper models that process everything as 30-second chunks. To give you an idea of the benefits: Moonshine processes 10-second audio segments 5x faster than Whisper while maintaining the same (or better!) WER.
Also for kyutai, we can input continuous audio in and get continuous text out.
- https://github.com/moonshine-ai/moonshine - https://docs.hyprnote.com/owhisper/configuration/providers/k...
Having used whisper and noticed the useless quality due to their 30-second chunks, I would stay far away from software working on even a shorter duration.
The short duration effectively means that the transcription will start producing nonsense as soon as a sentence is cut up in the middle.
Something like that, in a cli tool, that just gives text to stdout would be perfect for a lot of use cases for me!
(maybe with an `owhisper serve` somewhere else to start the model running or whatever.)
I wrote a tool that may be just the thing for you:
https://github.com/bikemazzell/skald-go/
Just speech to text, CLI only, and it can paste into whatever app you have open.
Oh, this does sound cool. Couple of questions that aren't clear from the readme (to me).
What exactly does the silence detection mean? does that mean it'll wait until a pause, and then send the audio off to whisper, and return the output (and stop the process)? Same question with continuous. Does that just mean it continues going until CTRL+C?
Nvm, answered my own question, looks like yes for both[0][1]. Cool this seems pretty great actually.
[0] https://github.com/bikemazzell/skald-go/blob/main/pkg/skald/...
[1] https://github.com/bikemazzell/skald-go/blob/main/pkg/skald/...
Are you thinking about the realtime use-case or batch use-case?
For just transcribing file/audio,
`owhisper run <MODEL> --file a.wav` or
`curl httpsL//something.com/audio.wav | owhisper run <MODEL>`
might makes sense.
agreed, both of those make sense, but I was thinking realtime. (pipes can stream data, I'd like and find useful something that can stream tts to stdout in realtime.)
It's open-source. Happy to review & merge if you can send us PR!
https://github.com/fastrepl/hyprnote/blob/8bc7a5eeae0fe58625...
FYI: owhisper pull whisper-cpp-large-turbo-q8 Failed to download model.ggml: Other error: Server does not support range requests. Got status: 200 OK
But the base-q8 works (and works quite well!). The TUI is really nice. Speaker diarization would make it almost perfect for me. Thanks for building this.
we store data in R2 and range query sometime glitch... It might work if you retry it
Sorry, maybe I missed it but I didn't see this list on your website. I think it is a good idea to add this info there. Besides that, thank you for the effort and your work! I will definetely give it a try
got it. fyi if you run `owhisper pull --help`, this info is printed
THIS IS THE BOMB!!! So excited for this one. Thanks for putting cool tech out there.
Thank you!
Is there a way to list all the models that are available to be pulled?
sure. `owhisper pull --help`
Just wanna give a shout out to the hyprnote team - I've been running it for about a month now and I love how simple and no gimmicks it is. It's a good app, def recommend! (Team seem like a lovely group of youngins' also) :)
Looks really cool! Will give it a try
Cool tool! Are you guys releasing Hyprnote for Windows this month?
probably end of this month or early next month. not 100% sure.
Is a container version in your roadmap?
Why cant it just use any OpenAI API endpoint?
what do you mean? this use-case is not llm. it is realtime stt.
also fyi - https://docs.hyprnote.com/owhisper/configuration/providers/o...
Looks similar to speaches
If you use this with a cloud provider, please consider using https://borgcloud.org/speech-to-text ( https://borgcloud.org/api/v1/audio/transcriptions - Open-AI-compatible endpoint ). We do transcription at $0.06 per hour using Whisper Large v3 Turbo. Deepgram.com is $0.288 per hour.
This has MPS support for hwa?
yes. metal is on
I suggest you don't brand this "Ollama for X". They've become a commercial operation that is trying to FOSS-wash their actions through using llama.cpp's code and then throwing their users under the bus when they can't support them.
I see that you are also using llama.cpp's code? That's cool, but make sure you become a member of that community, not an abuser.
yeah we use whisper.cpp for whisper inference. this is more like a community-focused project, not a commercial product!
Ya after spending a decent amount of time in r/localllama I was surprised that a project would want to name itself in association with Ollama, it’s got a pretty bad reputation in the community at this point.