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MOSS-Transcribe-Diarize

Name: MOSS-Transcribe-Diarize
Availability: InStock

transformers

pytorch

moss_transcribe_diarize

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MOSS-Transcribe-Diarize

MOSS-Transcribe-Diarize 0.9B is an end-to-end audio understanding model for long-form multi-speaker transcription, diarization, timestamps, and acoustic event awareness.

It supports transcription and diarization across 50+ languages, single-pass inference on audio recordings up to 90 minutes long, and custom hotword prompting for domain-specific terms.

Given an audio or video file, the model generates a compact speaker-aware transcript in one pass, including timestamps and anonymous speaker labels such as [S01], [S02], and beyond.

News

2026-07-14: 🏆 MOSS-Transcribe-Diarize won first place in the 2nd MLC-SLM Challenge at INTERSPEECH 2026, spanning 14 languages.
2026-07-09: Released MOSS-Transcribe-Diarize 0.9B.

Introduction
Model Architecture
Evaluation
Quickstart
Output Format
More Information
License
Citation

Introduction

MOSS-Transcribe-Diarize 0.9B turns real-world long-form audio into structured, speaker-aware transcripts in one pass. Instead of stitching together separate ASR and diarization systems, it jointly performs speech transcription and speaker diarization, producing time-aligned text with consistent speaker labels.

The model is built for meetings, calls, podcasts, interviews, lectures, videos, and other long or messy multi-speaker recordings. It can also emit acoustic event annotations, giving downstream systems a richer view of what happened, who spoke, and when.

Core capabilities:

Long-form transcription: Converts long audio or video recordings into timestamped text.
Speaker-aware diarization: Assigns anonymous speaker labels such as [S01] and [S02] without a separate diarization pipeline.
Promptable generation: Supports custom transcription instructions, hotwords, and acoustic event annotations.

Model Architecture

This Hugging Face repository includes the custom Transformers remote code required to load the model with trust_remote_code=True.

Evaluation

We evaluate MOSS-Transcribe-Diarize using three objective metrics: Character Error Rate (CER), concatenated minimum-permutation Character Error Rate (cpCER), and Delta-cp. Lower is better for all metrics. A dash (-) indicates that the result is unavailable.

Model	AISHELL‑4	Alimeeting	Podcast	Movies
CER↓	cpCER↓	Δcp↓	CER↓	cpCER↓	Δcp↓	CER↓	cpCER↓	Δcp↓	CER↓	cpCER↓	Δcp↓
Doubao	18.18	27.86	9.68	25.25	37.57	12.31	7.93	10.54	2.61	9.94	30.88	20.94
ElevenLabs	19.58	37.95	18.36	25.70	36.69	10.99	8.50	11.34	2.85	11.49	17.85	6.37
GPT-4o	-	-	-	-	-	-	-	-	-	14.37	23.67	9.31
Gemini 2.5 Pro	42.70	53.42	10.72	27.43	41.64	14.21	7.38	10.23	2.85	15.46	24.15	8.69
Gemini 3 Pro	22.75	27.43	4.68	26.75	32.84	6.09	-	-	-	8.62	14.73	6.11
VIBEVOICE ASR	21.40	24.99	3.59	27.40	29.33	1.93	27.94	48.30	20.36	14.59	42.54	27.94
MOSS Transcribe Diarize 0.9B	14.84	15.83	0.99	24.86	22.17	-2.69	5.97	7.37	1.40	6.36	12.76	6.40
MOSS Transcribe Diarize Pro	13.78	14.02	0.24	18.22	13.94	-4.27	4.46	6.97	2.51	5.86	11.78	5.92

Quickstart

Environment Setup

Use a clean Python environment. The model uses custom Transformers code, so load the model and processor with trust_remote_code=True.

bash

conda create -n moss-transcribe-diarize python=3.12 -y
conda activate moss-transcribe-diarize

git clone https://github.com/OpenMOSS/MOSS-Transcribe-Diarize.git
cd MOSS-Transcribe-Diarize

pip install --index-url https://download.pytorch.org/whl/cu128 torch torchaudio
pip install -e .

The GitHub package provides helper utilities such as audio/video loading, transcription message construction, transcript parsing, CLI inference, and the subtitle web app. The model weights and remote-code model files are loaded from this Hugging Face repository.

Python Usage

python

import torch
from transformers import AutoModelForCausalLM, AutoProcessor

from moss_transcribe_diarize import parse_transcript
from moss_transcribe_diarize.inference_utils import (
    build_transcription_messages,
    generate_transcription,
    resolve_device,
)

model_id = "OpenMOSS-Team/MOSS-Transcribe-Diarize"
audio_path = "audio.wav"

device = resolve_device("auto")
dtype = torch.bfloat16 if device.type == "cuda" else torch.float32

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    dtype="auto",
).to(dtype=dtype).to(device).eval()

processor = AutoProcessor.from_pretrained(
    model_id,
    trust_remote_code=True,
)

messages = build_transcription_messages(audio_path)
result = generate_transcription(
    model,
    processor,
    messages,
    max_new_tokens=2048,
    do_sample=False,
    device=device,
    dtype=dtype,
)

print(result["text"])

for segment in parse_transcript(result["text"]):
    print(segment.start, segment.end, segment.speaker, segment.text)

The message flow follows the common Qwen multimodal pattern:

processor.apply_chat_template(messages, tokenize=False) renders text with audio placeholders.
The helper utilities load audio waveforms from the same messages.
processor(text=text, audio=audios) computes Whisper input features and expands audio placeholders.
model.generate(...) produces timestamped transcription and diarization text.

Custom Prompt and Hotwords

The default prompt is optimized for timestamped transcription and speaker diarization:

请将音频转写为文本，每一段需以起始时间戳和说话人编号（[S01]、[S02]、[S03]…）开头，正文为对应的语音内容，并在段末标注结束时间戳，以清晰标明该段语音范围。

To add hotwords, append a short hint to the default prompt:

请将音频转写为文本，每一段需以起始时间戳和说话人编号（[S01]、[S02]、[S03]…）开头，正文为对应的语音内容，并在段末标注结束时间戳，以清晰标明该段语音范围。热词提示：热词1, 热词2, 热词3

More prompt recipes are available in the GitHub repository: https://github.com/OpenMOSS/MOSS-Transcribe-Diarize/blob/main/examples/prompts.md

Serve with SGLang and VLLM

The recommended way to serve MOSS-Transcribe-Diarize is SGLang Omni through the OpenAI-compatible /v1/audio/transcriptions endpoint. If you are using a CUDA 12 environment, SGLang is currently not supported; use vLLM instead. Install sglang-omni by following the installation guide, then download the model:

hf download OpenMOSS-Team/MOSS-Transcribe-Diarize

Serve the model:

bash

sgl-omni serve \
  --model-path OpenMOSS-Team/MOSS-Transcribe-Diarize \
  --port 8000 \
  --max-running-requests 16 \
  --cuda-graph-max-bs 16 \
  --mem-fraction-static 0.80

Use response_format=verbose_json when you need parsed speaker segments. json returns the raw transcript text only.

bash

curl -X POST http://localhost:8000/v1/audio/transcriptions \
  -F model=OpenMOSS-Team/MOSS-Transcribe-Diarize \
  -F file=@audio.wav \
  -F response_format=verbose_json

For longer multi-speaker audio, raise max_new_tokens so the decoder can finish the full diarized transcript:

bash

curl -X POST http://localhost:8000/v1/audio/transcriptions \
  -F model=OpenMOSS-Team/MOSS-Transcribe-Diarize \
  -F file=@audio.wav \
  -F response_format=verbose_json \
  -F max_new_tokens=65536

MOSS-Transcribe-Diarize also supports vLLM serving through the OpenAI-compatible transcription API. Use a pinned vLLM nightly build that includes the MOSS-Transcribe-Diarize model registration. Choose one of the following commands: for CUDA 12 environments, use cu129; for CUDA 13 environments, use cu130.

bash

uv pip install -U vllm \
  --torch-backend=auto \
  --extra-index-url https://wheels.vllm.ai/68b4a1d582818e67adc903bf1b8fc5a5447da2fa/cu129

or:

bash

uv pip install -U vllm \
  --torch-backend=auto \
  --extra-index-url https://wheels.vllm.ai/68b4a1d582818e67adc903bf1b8fc5a5447da2fa/cu130

vllm serve OpenMOSS-Team/MOSS-Transcribe-Diarize --trust-remote-code

bash

curl http://localhost:8000/v1/audio/transcriptions \
  -F model="OpenMOSS-Team/MOSS-Transcribe-Diarize" \
  -F file=@"audio.wav" \
  -F response_format="json" \
  -F temperature="0"

Subtitle Web App

The source package includes a local subtitle workflow for upload, review, subtitle export, and optional FFmpeg burn-in:

bash

mtd-subtitle-web \
  --model OpenMOSS-Team/MOSS-Transcribe-Diarize \
  --host 127.0.0.1 \
  --port 7860

Open http://127.0.0.1:7860, upload an audio/video file, review the parsed subtitle segments, then download JSON/SRT/ASS or burn an MP4 if ffmpeg and ffprobe are available on PATH.

For batch processing:

bash

mtd-subtitle /path/to/input.mp4 \
  --model OpenMOSS-Team/MOSS-Transcribe-Diarize \
  --out-dir runs/example \
  --render

Output Format

The canonical output format is:

[start_time][Sxx]transcribed speech[end_time]

Example:

[0.48][S01]Welcome everyone[1.66][12.26][S02]The new transcription pipeline is ready for evaluation[13.81][14.36][S01]Great, include the diarization results in the report[18.76]

In this format:

start_time and end_time are timestamps in seconds.
[S01], [S02], and similar labels are anonymous model-generated speaker labels.
Speaker labels are relative labels within the input audio and should not be interpreted as real speaker identities.

More Information

GitHub: https://github.com/OpenMOSS/MOSS-Transcribe-Diarize
MOSI.AI: https://mosi.cn
OpenMOSS: https://www.open-moss.com

License

MOSS-Transcribe-Diarize 0.9B is licensed under the Apache License 2.0.

Citation

If you use MOSS-Transcribe-Diarize 0.9B, please cite the technical report:

bibtex

@misc{moss_transcribe_diarize_2026,
  title={MOSS Transcribe Diarize Technical Report},
  author={{MOSI.AI}},
  year={2026},
  eprint={2601.01554},
  archivePrefix={arXiv},
  primaryClass={cs.SD},
  url={https://arxiv.org/abs/2601.01554}
}

xqiuresearch/MOSS-Transcribe-Diarize

MOSS-Transcribe-Diarize

News

Contents

Introduction

Model Architecture

Evaluation

Quickstart

Environment Setup

Python Usage

Custom Prompt and Hotwords

Serve with SGLang and VLLM

Subtitle Web App

Output Format

More Information

License

Citation

xqiuresearch/
MOSS-Transcribe-Diarize