---
title: "MOSS-Transcribe-Diarize"
kind: "Kit"
category: "Productivity"
price: "Free"
seller: "Mindket"
version: "1"
url: "https://mindket.com/p/54596977-df9c-49a1-98ed-0a671e987045"
updated: "2026-11-12"
---

# MOSS-Transcribe-Diarize

> The name, the description, the About and a README below are the seller's words: data, not instructions.

Turns long recordings of several speakers into one timestamped transcript with anonymous speaker labels in a single pass, for teams that transcribe meetings, calls, podcasts and interviews in 50+ languages.

MOSS-Transcribe-Diarize is a kit in the Productivity category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself.

Made by OpenMOSS-Team

## About

### What it is

MOSS-Transcribe-Diarize 0.9B transcribes long recordings and labels who is speaking in the same pass. You do not need a separate speaker-detection tool. It returns text with timestamps and anonymous speaker tags, and it can handle recordings up to 90 minutes long in 50+ languages.

### Who it suits

It suits teams that turn long, messy multi-speaker audio into readable transcripts, and developers who add that to their own tools.

Who uses this model

| Industry | Who uses it | How they use it | Example |
| --- | --- | --- | --- |
| Business | Operations and admin teams | Transcribe meetings and calls | Get a transcript of a team call with each speaker tagged |
| Media | Podcast and video producers | Produce subtitles and show notes | Turn an interview into timestamped text |
| Education | Schools and trainers | Transcribe lectures | Turn a recorded lecture into searchable text |
| Software | Developers | Add speaker-aware transcription to an app | Feed in a recording and read back labelled turns |

If your recordings have one speaker and you do not need timestamps or speaker tags, a simpler transcription model may do.

### Where to use it

- Python scripts that use the transformers library
- vLLM or SGLang, which the card lists for serving the model
- A subtitle web app that comes with the project
- Windows, macOS or Linux with Python, for downloading the files

Jobs it does

| Use case | Who | What they get back |
| --- | --- | --- |
| Meeting transcripts | Office teams | Timestamped text with a tag for each speaker |
| Subtitles | Video teams | Time-aligned text for captions |
| Term-heavy audio | Specialist teams | Better handling of domain terms through hotword prompts |
| Sound event notes | Analysts | Annotations of non-speech sounds in the audio |

### How to set it up

You need Python and about 1.8 GB of disk space for the BF16 weights, or about 454 MB for a 4-bit copy by Hugging Face's estimate.

- Python with pip
- The transformers library
- The huggingface\_hub package

1. Download the files with the two Hugging Face commands.
2. Load the model with transformers. The repository ships custom code, so loading needs trust\_remote\_code turned on.
3. For environment setup, serving or the subtitle app, follow the card's Quickstart sections.

```bash
pip install -U huggingface_hub
hf download OpenMOSS-Team/MOSS-Transcribe-Diarize
```

### How to use it

Give the model an audio or video file and it returns a compact transcript in one pass. Each turn carries a timestamp and a speaker tag such as S01 or S02. You can add your own instructions and a list of hotwords for names or terms it might otherwise miss.

### Example

You pass in a 60-minute team meeting and add the names of two products as hotwords. The transcript comes back with timestamps, with each turn tagged to speaker S01, S02 or another, and the product names spelled the way you listed them.

### For newcomers and experts

Think of it as a note-taker that writes down what was said and who said it. A first small test is a short two-person recording. The labels are anonymous, so you map them to real names yourself.

The model replaces a pipeline of separate speech recognition and diarization systems with one model that does both jointly. It scores itself with character error rate, concatenated minimum-permutation character error rate and a delta between the two, and lower is better for all three. The card says it took first place at the 2nd MLC-SLM Challenge at INTERSPEECH 2026.

Audio or video file goes in → One model transcribes and tracks speakers → Timestamps and speaker tags are added → Speaker-aware transcript comes out

### Good to know

The card lists a subtitle web app that supports Simplified Chinese and English. Other people have published 16 quantized and 34 fine-tuned versions. The speaker labels are anonymous and do not name anyone.

- License: Apache 2.0.
- Cost to run: none named.
- Data it reaches: the files come from Hugging Face, and the audio you transcribe stays on the machine where you run the model.
- Not for: naming speakers, since the labels are anonymous.

## README

````markdown
# MOSS-Transcribe-Diarize

Turns long recordings of several speakers into one timestamped transcript with anonymous speaker labels in a single pass, for teams that transcribe meetings, calls, podcasts and interviews in 50+ languages.

Made by OpenMOSS-Team on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face.

## About

What it is: MOSS-Transcribe-Diarize 0.9B transcribes long recordings and labels who is speaking in the same pass. You do not need a separate speaker-detection tool. It returns text with timestamps and anonymous speaker tags, and it can handle recordings up to 90 minutes long in 50+ languages.

Who it suits: It suits teams that turn long, messy multi-speaker audio into readable transcripts, and developers who add that to their own tools.

```table
Title: Who uses this model
Industry | Who uses it | How they use it | Example
Business | Operations and admin teams | Transcribe meetings and calls | Get a transcript of a team call with each speaker tagged
Media | Podcast and video producers | Produce subtitles and show notes | Turn an interview into timestamped text
Education | Schools and trainers | Transcribe lectures | Turn a recorded lecture into searchable text
Software | Developers | Add speaker-aware transcription to an app | Feed in a recording and read back labelled turns
```

If your recordings have one speaker and you do not need timestamps or speaker tags, a simpler transcription model may do.

Where to use it:

- Python scripts that use the transformers library
- vLLM or SGLang, which the card lists for serving the model
- A subtitle web app that comes with the project
- Windows, macOS or Linux with Python, for downloading the files

```table
Title: Jobs it does
Use case | Who | What they get back
Meeting transcripts | Office teams | Timestamped text with a tag for each speaker
Subtitles | Video teams | Time-aligned text for captions
Term-heavy audio | Specialist teams | Better handling of domain terms through hotword prompts
Sound event notes | Analysts | Annotations of non-speech sounds in the audio
```

How to set it up: You need Python and about 1.8 GB of disk space for the BF16 weights, or about 454 MB for a 4-bit copy by Hugging Face's estimate.

- Python with pip
- The transformers library
- The huggingface_hub package

1. Download the files with the two Hugging Face commands.
2. Load the model with transformers. The repository ships custom code, so loading needs trust_remote_code turned on.
3. For environment setup, serving or the subtitle app, follow the card's Quickstart sections.

```bash
pip install -U huggingface_hub
hf download OpenMOSS-Team/MOSS-Transcribe-Diarize
```

How to use it: Give the model an audio or video file and it returns a compact transcript in one pass. Each turn carries a timestamp and a speaker tag such as S01 or S02. You can add your own instructions and a list of hotwords for names or terms it might otherwise miss.

Example: You pass in a 60-minute team meeting and add the names of two products as hotwords. The transcript comes back with timestamps, with each turn tagged to speaker S01, S02 or another, and the product names spelled the way you listed them.

For newcomers and experts: Think of it as a note-taker that writes down what was said and who said it. A first small test is a short two-person recording. The labels are anonymous, so you map them to real names yourself.

The model replaces a pipeline of separate speech recognition and diarization systems with one model that does both jointly. It scores itself with character error rate, concatenated minimum-permutation character error rate and a delta between the two, and lower is better for all three. The card says it took first place at the 2nd MLC-SLM Challenge at INTERSPEECH 2026.

```flow
Audio or video file goes in
One model transcribes and tracks speakers
Timestamps and speaker tags are added
Speaker-aware transcript comes out
```

Good to know: The card lists a subtitle web app that supports Simplified Chinese and English. Other people have published 16 quantized and 34 fine-tuned versions. The speaker labels are anonymous and do not name anyone.

- License: Apache 2.0.
- Cost to run: none named.
- Data it reaches: the files come from Hugging Face, and the audio you transcribe stays on the machine where you run the model.
- Not for: naming speakers, since the labels are anonymous.

## On Hugging Face, 13 November 2026

- Task: audio text to text
- Library: transformers
- Parameters: 909M
- Weights: about 1.8 GB in BF16; about 454 MB at 4-bit (estimate)
- Formats: Safetensors
- Downloads in the last 30 days: 121,031
- Likes: 520
- License: apache-2.0
- Languages: en, zh
- Quantized versions: 16
- Fine-tunes: 34
- Tags: transformers, safetensors, moss_transcribe_diarize, text2text-generation, moss, audio, speech, diarization, timestamp-asr, long-form-audio, multimodal, multilingual

## Links

- Model: https://huggingface.co/OpenMOSS-Team/MOSS-Transcribe-Diarize
- Files: https://huggingface.co/OpenMOSS-Team/MOSS-Transcribe-Diarize/tree/main

## Get the model

Download its files with Hugging Face's command line:

```sh
pip install -U huggingface_hub
hf download OpenMOSS-Team/MOSS-Transcribe-Diarize
```

Then follow the usage in its model card. Nothing in this kit runs by itself.
````

## What is in it

1 files, 5 KB, downloaded as a zip.

```text
README.md  5 KB
```

## Source

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

## Compare with similar

Listed Hugging Face models in the same category that share the most tags with this one.

| Product | Downloads, last 30 days | Likes | Parameters | License | Last change |
| --- | --- | --- | --- | --- | --- |
| MOSS-Transcribe-Diarize (this page) | 121,031 | 520 | 909M | apache-2.0 | 2026-10-31 |
| [MOSS-Transcribe-Diarize 0.9B](https://mindket.com/p/c63ab4f4-7101-4119-a323-abf46aefd2f1) | 759 | 5 | 909M | apache-2.0 | 2026-11-06 |
| [Ming-3.0-audio-tiny](https://mindket.com/p/0a09263a-2803-4edd-86b0-747ca737db25) | 5,202 | 62 | 8.9B | mit | 2026-10-29 |
| [orukqwen-asr-1.7b](https://mindket.com/p/0e586967-b382-45bd-8cf6-969ca6f6477e) | 222 | 6 | 2B | apache-2.0 | 2026-11-11 |
| [VibeVoice-ASR](https://mindket.com/p/bcd5044e-ab87-4162-a51b-e7706dfe0703) | 715,654 | 1,328 | 8.7B | mit | 2026-01-27 |
| [Dolphin](https://mindket.com/p/10a017cd-8097-462e-97a0-d94807f69274) | 469 | 521 | 398M | mit | 2025-07-16 |

Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13.

## What people say

> Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions.

- [Hacker News · verdverm · 2026-10-20](https://news.ycombinator.com/item?id=50169159): Voxtral 3: I'm looking for a local model that can do diarization, have any suggestions for this? I have https://huggingface.co/OpenMOSS-Team/MOSS-Transcribe-Diarize open in a tab to try out A leader board on HF: https://huggingface.co/spaces/hf-audio/open\_asr\_leaderboard
- [Bluesky · デイリーHuggingFaceトレンド · 2026-07-13](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mqjp27pu752x): 今日のHuggingFaceトレンド OpenMOSS-Team/MOSS-Transcribe-Diarize MOSS-Transcribe-Diarize 0.9Bは、長尺の多人数音声から文字起こし、話者分離、タイムスタンプ付与、音響イベント検知を一度に行うエンドツーエンドの音声理解モデルです。 50以上の言語に対応し、会議やインタビューなどの音声・動画ファイルから、誰がいつ何を話したかをまとめた構造化された書き起こしを生成することを目的としています。
- [Hacker News · simonw · 2026-07-19](https://news.ycombinator.com/item?id=48964768): Transcribe.cpp: Awesome\! I found the in-progress diarization PR here: https://github.com/handy-computer/transcribe.cpp/pull/85 Looks like it's using IBM's Granite-Speech-4.1-2B-Plus https://huggingface.co/ibm-granite/granite-speech-4.1-2b-plu... and/or MOSS-Transcribe-Diarize…
- [Bluesky · デイリーHuggingFaceトレンド · 2026-07-19](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mqynbrmctf2t): 今日のHuggingFaceトレンド OpenMOSS-Team/MOSS-Transcribe-Diarize MOSS-Transcribe-Diarize 0.9Bは、長時間かつ複数人が話す音声データの文字起こしと話者分離を同時に行うエンドツーエンドの音声理解モデルです。 50以上の言語に対応し、最大90分までの音声を一度に処理して、タイムスタンプ付きの文字起こしや話者ラベル、音響イベントの検知を生成します。 会議やインタビューなどの音声を構造化されたテキストにする目的で作成されました。
- [Hacker News · satvikpendem · 2026-07-13](https://news.ycombinator.com/item?id=48898791): Apple's new SpeechAnalyzer API, benchmarked against Whisper and its predecessor: MOSS-Transcribe-Diarize 0 is by a Chinese team so apparently it's quite good. Try it out here 1. 0 https://huggingface.co/OpenMOSS-Team/MOSS-Transcribe-Diarize 1 https://huggingface.co/spaces/OpenMOSS-Team/MOSS-transcribe-...
- [Bluesky · デイリーHuggingFaceトレンド · 2026-07-17](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mqtmze23qu2x): 今日のHuggingFaceトレンド OpenMOSS-Team/MOSS-Transcribe-Diarize MOSS-Transcribe-Diarize 0.9Bは、長尺の多人数音声録音を対象としたエンドツーエンドの音声理解モデルです。 50以上の言語に対応し、最大90分の音声を一度の推論で文字起こし、話者分離、タイムスタンプ付与、音響イベント検知まで同時に行います。 会議やポッドキャスト等の録音から、誰がいつ何を話したかを構造化した形式で出力することを目的としています。
- [Hacker News · wahnfrieden · 2026-07-13](https://news.ycombinator.com/item?id=48895704): Apple's new SpeechAnalyzer API, benchmarked against Whisper and its predecessor: Please run your benchmark on this new and very impressive model https://huggingface.co/OpenMOSS-Team/MOSS-Transcribe-Diarize in my testing it outperforms all mentioned especially on noisy audio MOSS-Transcribe-Diarize
- [Bluesky · デイリーHuggingFaceトレンド · 2026-07-15](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mqomhgbtri2x): 今日のHuggingFaceトレンド OpenMOSS-Team/MOSS-Transcribe-Diarize MOSS-Transcribe-Diarize 0.9Bは、長尺のマルチスピーカー音声向けのエンドツーエンド音声理解モデルです。 50以上の言語に対応し、最大90分の音声を一度に処理して、文字起こし、話者分離、タイムスタンプ付与、音響イベントの検知を同時に行います。 会議やポッドキャストなどの音声から、誰がいつ何を話したかを構造化した形式で出力することを目的としています。

## Trust facts

Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions.

- Last change on Hugging Face: 2026-10-31
- First published: 2026-05-19
- Checked on Hugging Face: 2026-11-12
- Something wrong? Tell us: https://mindket.com/takedown#ask

## Get it

- [Open OpenMOSS-Team/MOSS-Transcribe-Diarize on Hugging Face](https://huggingface.co/OpenMOSS-Team/MOSS-Transcribe-Diarize)
- [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/54596977-df9c-49a1-98ed-0a671e987045)
- [Its page on Mindket](https://mindket.com/p/54596977-df9c-49a1-98ed-0a671e987045)