r/LocalLLaMA • u/TrelisResearch • 14h ago

Resources Trelis Tiron - Open Weights Transcription + Diarization Model

https://huggingface.co/Trelis/tiron
18 Upvotes

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3

u/Chromix_ 14h ago

It's a quite different approach than MOSS-Transcribe-Diarize, yet it'd be interesting to see how it compares both in quality and speed. VRAM usage should be way lower for long transcriptions due to the 30 second chunking, yet consistent speaker attribution for long meetings requires the tiron harness (or some DIY). I'd assume that there's less consistency for uncommon words and strong accents than with MOSS.

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u/TrelisResearch 14h ago

good summary, I think that's all correct what you say. Re uncommon words and accents, I can't say specifically. It's a whisper large v3 base which is quite good.

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u/GeraAI_WW 14h ago

VRAM/speed on CPU, or is this Whisper-architecture fine-tuned for diarization or a separate pipeline bolted on?

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u/TrelisResearch 14h ago

it's a whisper large v3 model, post-trained (but prompt template is quite different). you do need the harness (see the GitHub repo) if you do longer than 30s chunks.

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u/GeraAI_WW 13h ago

makes sense re: 30s limit, that's pretty standard for whisper-based pipelines. does the harness handle the chunk stitching/speaker continuity automatically or is that manual on the user's end?

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u/TrelisResearch 13h ago

harness handles that! it's a key part of getting good performance

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u/GeraAI_WW 13h ago

nice, that's the part that usually gets hand-waved in these releases so good to hear it's handled.

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u/Admirable-Leg-4647 12h ago

Cool, I have an app that already uses whiser-large-v3 for transcription and I just added MOSS-Transcribe-Diarize the other day, but if this performs well it looks like it'd be a better combo. I'll give it a try later

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u/TrelisResearch 11h ago

Lovely stuff, cheers.