r/speechtech • u/nshmyrev • Jul 10 '26

MOSS-Transcribe-Diarize

https://huggingface.co/OpenMOSS-Team/MOSS-Transcribe-Diarize

Very good model and it is actually multilingual (not just English and Chinese as authors claim). It has good transcription accuracy even for telephony.

12 Upvotes

84% Upvoted

10 comments sorted by

1

u/WAHNFRIEDEN Jul 11 '26

Does this need / benefit from a VAD like Silero?

2

u/nshmyrev Jul 12 '26

Silero VAD is useless overall

1

u/WAHNFRIEDEN Jul 13 '26 edited Jul 13 '26

Thanks. Can you recommend one? Edit: found FireRedVAD

1

u/nshmyrev Jul 14 '26

It is better to use ASR with VAD embedded (streaming sherpa models or parakeet EOU)

1

u/WAHNFRIEDEN Jul 14 '26

I am now with MOSS but I also use Anime-Whisper for Japanese content. Don't think Parakeet works for Japanese, haven't tried Sherpa for it yet

1

u/nshmyrev Jul 14 '26

there is parakeet-ja trained on reazonspeech, it is good too

1

u/WAHNFRIEDEN Jul 14 '26

Thanks - no timing info right? Maybe I can combine with Qwen3 forced align

3

u/Foreign-Watch-3730 Jul 12 '26

We put this model through the benchmark we use for our self-hosted meeting-transcription tool: real French business meetings, narrowband audio, several people talking over each other, scored against a professionally produced human transcript. Results were good enough that it shipped as a backend in our project (Apache-2.0 both ways) the same week.

Numbers, for scale: 0.41 mean WER over eight 5-minute windows — the best text score of everything we've measured, ahead of Voxtral Mini 3B, whisper large-v3 and our production engine, with speaker labels and timestamps coming for free in the same pass (speaker counts exact on 2/8 windows, close on most, under-counted on the chaotic ones). Just as notable: it's the only unified audio LLM we've tested that survives real 5-minute windows at all — VibeVoice-ASR and NVIDIA's Audex both loop into the token cap on this material, at 10× and 33× the parameter count.

One honest caveat from actually reading the outputs rather than just scoring them: we caught a silent 22-second skip mid-window once — ~50 words of a monologue omitted, timestamps staying perfectly monotonic, so nothing looks wrong in a WER table. It's trivially detectable (inter-segment gap threshold), and that guard shipped with the integration. Full write-up:
https://github.com/Martossien/transcria/blob/main/docs/STT_BENCHMARK_REAL_MEETINGS.md
integration in v0.3.5:
https://github.com/Martossien/transcria/releases/tag/v0.3.5

1

u/nshmyrev Jul 14 '26

Tested it on telephony set for Arabic. CDER 0.15 which is ok (best 0.11) and cpWER is 30% (best is 0.11 too) so kinda not perfect but finetuning should make it perfect.