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.

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.

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.

Component	Specification
Text backbone	Qwen3-0.6B style causal decoder
Audio encoder	Whisper-Medium encoder configuration
Audio frontend	WhisperFeatureExtractor, 16 kHz, 80 mel bins, 30 s chunks
Audio-text bridge	4x temporal merge + MLP adaptor
Fusion	Audio features replace `<
Output format	Compact [start][Sxx]text[end] transcript with speaker tags such as [S01]

GGUF:

https://huggingface.co/mudler/moss-transcribe.cpp-gguf