Show HN: Moonshine Open-Weights STT models – higher accuracy than WhisperLargev3

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.

Show HN: Moonshine Open-Weights STT models – higher accuracy than WhisperLargev3
github.com

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.

How does it compare to Microsoft VibeVoice ASR https://news.ycombinator.com/item?id=46732776 ?

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)

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.