OpenMOSS/MOSS-Transcribe-Diarize

Health v2 · maintenance only

Flags: no_releases young

Adoption not part of the score

2064 stars · 127 forks observed · 2026-09-24

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

MOSS-Transcribe-Diarize 0.9B is an open-source end-to-end audio understanding model that jointly performs multi-speaker speech transcription and speaker diarization with timestamps and acoustic event annotations. It supports 50+ languages and includes serving options (SGLang, vLLM) and a subtitle-generating web UI.

Use cases

• transcribe long multi-speaker meetings with speaker labels
• diarize podcasts and interviews with timestamps
• generate subtitles from video or lecture recordings
• transcribe multilingual audio in 50+ languages
• detect acoustic events in recordings alongside transcripts
• transcribe phone calls with consistent speaker labels

When to choose

• you need joint ASR and diarization in a single pass instead of stitching separate systems
• you work with long, messy, multi-speaker recordings like meetings, calls, or podcasts
• you need open-source, self-hosted transcription with timestamps and speaker labels
• you need multilingual transcription across 50+ languages

When to avoid

• you only need simple single-speaker dictation where a lightweight ASR suffices
• you have no GPU or inference infrastructure available
• you need a fully managed hosted service rather than a self-run model
• you need real-time streaming transcription rather than long-form batch processing

Facets

library · maturity active

speech-recognition machine-learning llm-inference audio-processing speech-processing machine-learning python asr diarization speaker-labeling timestamps transcription multilingual subtitles long-form-audio natural-language-processing audio gpu linux docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="OpenMOSS/MOSS-Transcribe-Diarize")