MOSS-Transcribe-Diarize

MOSS-Transcribe-Diarize 0.9B 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 (English, French, German, Italian, Portuguese, Spanish, Japanese, Korean, Russian, Thai, Vietnamese, Tagalog, Urdu, Turkish). 2026-07-09: Released MOSS-Transcribe-Diarize 0.9B. Contents Introduction Model Architecture Evaluation Quickstart Environment Setup Python Usage Serve with vLLM and SGLang Subtitle Web App 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.