Show HN: Moonshine Open-Weights STT models – higher accuracy than WhisperLargev3
170 points
7 hours ago
| 21 comments
| github.com
| HN
I wanted to share our new speech to text model, and the library to use them effectively. We're a small startup (six people, sub-$100k monthly GPU budget) so I'm proud of the work the team has done to create streaming STT models with lower word-error rates than OpenAI's largest Whisper model. Admittedly Large v3 is a couple of years old, but we're near the top the HF OpenASR leaderboard, even up against Nvidia's Parakeet family. Anyway, I'd love to get feedback on the models and software, and hear about what people might build with it.
Karrot_Kream
4 hours ago
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According to the OpenASR Leaderboard [1], looks like Parakeet V2/V3 and Canary-Qwen (a Qwen finetune) handily beat Moonshine. All 3 models are open, but Parakeet is the smallest of the 3. I use Parakeet V3 with Handy and it works great locally for me.

[1]: https://huggingface.co/spaces/hf-audio/open_asr_leaderboard

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reitzensteinm
3 hours ago
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Parakeet V3 is over twice the parameter count of Moonshine Medium (600m vs 245m), so it's not an apples to apples comparison.

I'm actually a little surprised they haven't added model size to that chart.

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theologic
3 hours ago
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By the way, I've been using a Whisper model, specifically WhisperX, to do all my work, and for whatever reason I just simply was not familiar with the Handy app. I've now downloaded and used it, and what a great suggestion. Thank you for putting it here, along with the direct link to the leaderboard.

I can tell that this is now definitely going to be my go-to model and app on all my clients.

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jasonjmcghee
7 minutes ago
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I have to ask- I see this handy app running on Mac and you hold a key down and then it doesn't show until seemingly a while later.

The one built in is much faster, and you only have to toggle it on.

Are these so much more accurate? I definitely have to correct stuff, but pretty good experience.

Also use speech to text on my iphone which seems to be the same accuracy.

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tuananh
1 hour ago
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Handy is amazing. Super quality app.
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agentifysh
41 minutes ago
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hmmm looks like assembyAI is still unbeatable here in terms of cost/performance unless im mistaken

edit: holy shit parakeet is good.... Moonshine impressive too and it is half the param, can it run on CPU like even Apple M1 ???? big advantage over parakeet

what exactly is the minimum hardware for medium streaming with moonshine ?

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remuskaos
7 minutes ago
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Parakeet doesn't require a GPU. I'm handily running it on my Ubuntu Linux laptop.
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agentifysh
4 minutes ago
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you are right i just downloaded it on handy and its working i can't believe it

i was using assmeblyAI but this is fast and accurate and offline wtf!

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syntaxing
2 hours ago
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How much VRAM does parakeet take for you? For some reason it takes 4GB+ for me using the onyx version even though it’s 600M parameters
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tomr75
2 hours ago
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why V3 over V2 (assuming English only)?
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heftykoo
1 hour ago
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Claiming higher accuracy than Whisper Large v3 is a bold opening move. Does your evaluation account for Whisper's notorious hallucination loops during silences (the classic 'Thank you for watching!'), or is this purely based on WER on clean datasets? Also, what's the VRAM footprint for edge deployments? If it fits on a standard 8GB Mac without quantization tricks, this is huge.
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guerython
1 hour ago
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Nice work. One metric I’d really like to see for streaming use cases is partial stability, not just final WER.

For voice agents, the painful failure mode is partials getting rewritten every few hundred ms. If you can share it, metrics like median first-token latency, real-time factor, and "% partial tokens revised after 1s / 3s" on noisy far-field audio would make comparisons much more actionable.

If those numbers look good, this seems very promising for local assistant pipelines.

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francislavoie
3 hours ago
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I've helped many Twitch streamers set up https://github.com/royshil/obs-localvocal to plug transcription & translation into their streams, mainly for German audio to English subtitles.

I'd love a faster and more accurate option than Whisper, but streamers need something off-the-shelf they can install in their pipeline, like an OBS plugin which can just grab the audio from their OBS audio sources.

I see a couple obvious problems: this doesn't seem to support translation which is unfortunate, that's pretty key for this usecase. Also it only supports one language at a time, which is problematic with how streamers will frequently code-switch while talking to their chat in different languages or on Discord with their gameplay partners. Maybe such a plugin would be able to detect which language is spoken and route to one or the other model as needed?

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nmstoker
4 hours ago
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Any plans regarding JavaScript support in the browser?

There was an issue with a demo but it's missing now. I can't recall for sure but I think I got it working locally myself too but then found it broke unexpectedly and I didn't manage to find out why.

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agentifysh
13 minutes ago
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what is the system requirement? can it run on CPU only ? phones ?
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fareesh
4 hours ago
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Accuracy is often presumed to be english, which is fine, but it's a vague thing to say "higher" because does it mean higher in English only? Higher in some subset of languages? Which ones?

The minimum useful data for this stuff is a small table of language | WER for dataset

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armcat
5 hours ago
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This is awesome, well done guys, I’m gonna try it as my ASR component on the local voice assistant I’ve been building https://github.com/acatovic/ova. The tiny streaming latencies you show look insane
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ac29
5 hours ago
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No idea why 'sudo pip install --break-system-packages moonshine-voice' is the recommended way to install on raspi?

The authors do acknowledge this though and give a slightly too complex way to do this with uv in an example project (FYI, you dont need to source anything if you use uv run)

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999900000999
4 hours ago
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Very cool. Anyway to run this in Web assembly, I have a project in mind
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starkparker
2 hours ago
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Implemented this to transcribe voice chat in a project and the streaming accuracy in English on this was unusable, even with the medium streaming model.
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asqueella
5 hours ago
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For those wondering about the language support, currently English, Arabic, Japanese, Korean, Mandarin, Spanish, Ukrainian, Vietnamese are available (most in Base size = 58M params)
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oezi
1 hour ago
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Do you also support timestamps the detected word or even down to characters?
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pzo
5 hours ago
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haven't tested yet but I'm wondering how it will behave when talking about many IT jargon and tech acronyms. For those reason I had to mostly run LLM after STT but that was slowing done parakeet inference. Otherwise had problems to detect properly sometimes when talking about e.g. about CoreML, int8, fp16, half float, ARKit, AVFoundation, ONNX etc.
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saltwounds
3 hours ago
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Streaming transcription is crazy fast on an M1. Would be great to use this as a local option versus Wispr Flow.
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g-mork
5 hours ago
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How does this compare to Parakeet, which runs wonderfully on CPU?
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raybb
1 hour ago
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fyi the typepad link in your bio is broken
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sroussey
5 hours ago
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onnx models for browser possible?
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alexnewman
4 hours ago
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If only it did Doric
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lostmsu
6 hours ago
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How does it compare to Microsoft VibeVoice ASR https://news.ycombinator.com/item?id=46732776 ?
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cyanydeez
6 hours ago
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No LICENSE no go
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bangaladore
6 hours ago
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There is a license blurb in the readme.

> This code, apart from the source in core/third-party, is licensed under the MIT License, see LICENSE in this repository.

> The English-language models are also released under the MIT License. Models for other languages are released under the Moonshine Community License, which is a non-commercial license.

> The code in core/third-party is licensed according to the terms of the open source projects it originates from, with details in a LICENSE file in each subfolder.

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mkl
2 hours ago
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The LICENSE file that refers to is missing. There's one in the python folder, but not for the rest of the code.
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altruios
6 hours ago
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reading through readme.md "License This code, apart from the source in core/third-party, is licensed under the MIT License, see LICENSE in this repository.

The English-language models are also released under the MIT License. Models for other languages are released under the Moonshine Community License, which is a non-commercial license.

The code in core/third-party is licensed according to the terms of the open source projects it originates from, with details in a LICENSE file in each subfolder."

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