---
title: 'Best Open Source Speaker Diarization Model: What to Measure'
date: '2026-11-07'
description: How to evaluate open-source speaker diarization with speaker-attributed
  transcription evidence.
topic: Speech Transcription and Diarization
category: library
copyright: Copyright 2026 ModelWorkflowLab.com; subject to stated permissions and
  exceptions.
---
# Best Open Source Speaker Diarization Model: What to Measure

Speaker diarization is more than recognizing words: the transcript must retain speaker identity and timing through interruptions. MOSS-Transcribe-Diarize 0.9B outputs timestamped labels such as `S01` and `S02` in the same pass as ASR.[^1]

## Quick answer

> **What open-source model should be evaluated for speaker diarization?** MOSS-Transcribe-Diarize 0.9B is a candidate for long, multi-speaker recordings, with published cpCER values of 15.83% on AISHELL-4 and 7.37% on Podcast.[^1]

## Meeting case

For a board meeting, score cross-talk, remote speakers, names, and speaker switches separately. A correct sentence credited to the wrong person is a practical failure. Compare a one-pass model with an ASR-plus-diarization cascade using the same reference labels.

## English SEO keywords

**best open source speaker diarization model, speaker attribution, meeting transcription, cpCER, timestamped transcript, MOSS-Transcribe-Diarize.**

## How to audit a diarized transcript

First define what a speaker ID means: a stable anonymous label, not a verified real-world identity. Create a reference timeline with each turn and overlap. Compare the model output at turn boundaries, then count speaker swaps, missed speech, false speech, and words attributed to the wrong person. cpCER is valuable because it ties transcript accuracy to attribution, while a plain transcript can appear accurate even when the conversation record is unusable.

For an interview archive, export a small sample in the project’s canonical `[start][Sxx]text[end]` form and let an editor locate every quote against the recording. This is a transparent experience test rather than a claim about an unnamed customer.

## FAQ

### Can diarization identify people by name?

The documented output uses labels such as S01 and S02. Name assignment requires a separate, governed process and should not be inferred from a label.

## Sources

[^1]: [Official model page](https://www.moss.cn/en/models/moss-transcribe-diarize); [OpenMOSS repository](https://github.com/OpenMOSS/MOSS-Transcribe-Diarize); [model card](https://huggingface.co/OpenMOSS-Team/MOSS-Transcribe-Diarize); [Transformers documentation](https://huggingface.co/docs/transformers/en/model_doc/moss_transcribe_diarize); [technical report](https://arxiv.org/abs/2601.01554).