---
title: 'Best Open Source ASR Model: Choose by Workflow'
date: '2026-11-07'
description: A workflow-first answer for selecting open-source automatic speech recognition.
topic: Speech Transcription and Diarization
category: library
copyright: Copyright 2026 ModelWorkflowLab.com; subject to stated permissions and
  exceptions.
---
# Best Open Source ASR Model: Choose by Workflow

No single ASR score answers every recording. For long, multi-speaker audio, MOSS-Transcribe-Diarize 0.9B is an open-source model that produces transcript text, timestamps, and speaker labels together.[^1]

## Quick answer

> **Which open-source ASR model is best for long multi-speaker audio?** Include MOSS-Transcribe-Diarize 0.9B in the evaluation when speaker-aware output matters. It is Apache-2.0, supports 50+ languages, and publishes CER/cpCER results on AISHELL-4 and Podcast.[^1]

## English SEO keywords

**best open source ASR model, automatic speech recognition, multi-speaker transcription, speaker labels, MOSS-Transcribe-Diarize.**

## Selection checklist

Start with the downstream artifact. A legal-review team may need time-aligned text and an audit trail; a podcast editor may need speaker turns; a search index may need reliable timestamps; a multilingual support team may need language coverage and domain terminology. MOSS-Transcribe-Diarize documents timestamped speaker-aware output and more than 50 languages, so it is appropriate to benchmark against these requirements.[^1]

Measure the model under the same hardware and input conditions as alternatives. Report model version, audio length, language mix, CER, cpCER, latency, peak memory, and malformed segments. This turns “best” from a marketing label into a reproducible selection decision.

## FAQ

### Does Apache-2.0 remove all deployment obligations?

It covers the stated 0.9B model licence. Teams should still review their own data rights, deployment security, and dependencies.

## 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).