# OpenMOSS Releases Transcribe-Diarize ASR Model

> The open-weights team behind MOSS turns to long-form speech recognition with built-in speaker diarization and timestamps.

Published by The Open Weights on May 19, 2026. Canonical: https://theopenweights.com/news/moss-transcribe-diarize-xcc6

## Key facts

- Company: OpenMOSS
- Model: MOSS Transcribe-Diarize
- Version: 1.0
- Category: Speech → Text
- Modalities: Speech → Text
- License: Other (Open weights, commercial use allowed)
- Architecture: MossTranscribeDiarizeForConditionalGeneration
- Precision: BF16
- Weights size: 1.8 GB
- Significance: notable
- Published: 2026-05-19
- Last verified: 2026-07-26
- Hugging Face: https://huggingface.co/OpenMOSS-Team/MOSS-Transcribe-Diarize
- Canonical URL: https://theopenweights.com/news/moss-transcribe-diarize-xcc6

OpenMOSS has published [MOSS Transcribe-Diarize](https://huggingface.co/OpenMOSS-Team/MOSS-Transcribe-Diarize), an open-weights speech recognition model aimed squarely at the messy realities of real-world audio: long recordings, multiple speakers, and the need to know who said what and when.

Unlike a plain transcription model that spits out an undifferentiated wall of text, this release combines three tasks that usually require stitching together separate tools. It transcribes long-form audio, applies speaker diarization to separate distinct voices, and produces timestamped output so passages can be aligned back to the original recording.

## Why it matters

Diarization is one of the most persistent pain points in applied speech work. Teams building meeting summarizers, podcast tools, call-center analytics, or interview transcription pipelines typically bolt a diarization system onto a separate ASR engine, then fight to reconcile the two. Packaging these capabilities in a single open model lowers that friction considerably.

- Long-form transcription for extended recordings
- Speaker diarization to attribute speech to individual voices
- Timestamped output for alignment and navigation

The model ships under a custom license, so teams evaluating it for production should read the terms on the [model page](https://huggingface.co/OpenMOSS-Team/MOSS-Transcribe-Diarize) closely. As a 1.0 initial release, it also arrives without published benchmarks in the record, meaning the practical proof will come from real deployments. Still, for a space long dominated by a handful of proprietary APIs, another capable open option is worth attention.

## Get the model

- [Hugging Face](https://huggingface.co/OpenMOSS-Team/MOSS-Transcribe-Diarize)

## Sources

- [OpenMOSS-Team/MOSS-Transcribe-Diarize](https://huggingface.co/OpenMOSS-Team/MOSS-Transcribe-Diarize) — Hugging Face, May 19, 2026

---
Source: The Open Weights (https://theopenweights.com/). Aggregated and written by Claude, curated by humans. Cite as: "OpenMOSS Releases Transcribe-Diarize ASR Model", The Open Weights, May 19, 2026, https://theopenweights.com/news/moss-transcribe-diarize-xcc6