Yichi Zhang

6 posts

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Yichi Zhang

Yichi Zhang

@YichiZ03

Core Dev @ SGLang Omni https://t.co/IdEr7P9ImU

Katılım Haziran 2026
19 Takip Edilen122 Takipçiler
Yichi Zhang
Yichi Zhang@YichiZ03·
Thank you Daniel for trying MOSS-Transcribe-Diarize — and for such a detailed, generous writeup. The MTD model's core strength is long audio input, and the test case is exactly what it is designed for. Your engineering notes are really valuable to us. Really appreciate you pushing the model. Feel free to open issues or reach out — we'd love to keep in touch.
Daniel van Strien@vanstriendaniel

This week @Open_MOSS released MOSS-Transcribe-Diarize, a 0.9B open model (Apache 2.0) that transcribes, diarizes, and timestamps in a single pass. I used it to make 174 hours of Apollo 11 mission audio searchable by who said what, when. Total cost: $9.46. These are the real NASA tapes from July 1969, hosted by the @internetarchive. All 103 run through one @huggingface Job: a100-large, 3.8h, 47x realtime, @sgl_project serving the model inside the job. 45,355 timestamped speaker segments. Search the mission and hear any moment from the original tape! Space: huggingface.co/spaces/davanst…

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Yichi Zhang
Yichi Zhang@YichiZ03·
MOSS-Transcribe-Diarize-0.9B is out — congrats to @MosiAI_Official 🎉 Two things stand out: 90 min of audio in a single pass, and multi-speaker transcription. Both push hard on the serving stack. Day-0 support in SGLang-Omni — run it today on cookbook: sgl-project.github.io/sglang-omni/co…
MOSI@MosiAI_Official

🤗 MOSS-Transcribe-Diarize-0.9B is now open source on @huggingface. Built with an end-to-end audio-to-structured-transcript paradigm: >0.9B open-source ASR model >Apache license 2.0 >128k long-context transcription >Up to ~90-min audio input >Speaker labels + timestamps in one generation >Multi-speaker diarization for meetings, interruptions, and overlapping voices >Hotword biasing for names, terms, and domain-specific vocabulary >~100 token/s on NVIDIA RTX 4090, RTF ~0.017 Thank you @sgl_project @vllm_project @Prince_Canuma @lllucas for day-0 support! 🚀 Github: github.com/OpenMOSS/MOSS-… Huggingface: huggingface.co/spaces/OpenMOS… API: shorturl.at/DWwe3 Live demo: shorturl.at/wRZ3j Technical Report:arxiv.org/abs/2601.01554 HF Space: huggingface.co/spaces/OpenMOS… AtomGit:ai.atomgit.com/OpenMOSS/MOSS-… SGLang-Omni: github.com/sgl-project/sg… vLLM: github.com/vllm-project/v… MLX-audio: github.com/Blaizzy/mlx-au… Discord:discord.gg/SmVQHGffZU

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Yichi Zhang
Yichi Zhang@YichiZ03·
People may think serving and optimizing TTS (Text to Speech) model is similar to traditional LLM. It isn't. We made Higgs and MOSS-TTS 2–3.4× faster in SGLang-Omni — and most of the wins landed outside the LLM backbone. Delay patterns, D2H stalls, a vocoder tail-latency killer... In this post, we share everything we learned — every optimization, and every trade-off behind it. We hope it proves a valuable resource for developers building in this space. Full breakdown: x.com/YichiZ03/statu… @sgl_project
Yichi Zhang@YichiZ03

x.com/i/article/2074…

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Yichi Zhang
Yichi Zhang@YichiZ03·
A year ago when I just graduated, I thought AI was overhyped and thought I'd never touch it. Today I'm shipping AI infra for SGLang-Omni. I wrote up everything I figured out — why I switched, what AI infra actually does, and the exact methods that got me from zero to landing real PRs on a huge open-source codebase. If you're a SWE eyeing this space, this is the guide I wish I had and hopefully useful to you: x.com/YichiZ03/statu…
Yichi Zhang@YichiZ03

x.com/i/article/2073…

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