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0.9B model transcribes and diarizes 4-person meeting on MacBook in 12 mins

By PulseAugur Editorial · [1 sources] ·

A user on Reddit shared their experience running a 0.9B parameter model for transcription and speaker diarization on a MacBook Pro. The model processed a 17.5-minute, four-person meeting in just over 12 minutes, achieving a low speaker confusion rate of 0.08%. The model successfully identified all four speakers and handled instances of people talking over each other, producing overlapping lines with correct speaker attribution. AI

IMPACT Demonstrates the capability of smaller, locally runnable models for real-time meeting transcription and speaker identification.

RANK_REASON User-reported performance of a specific model on consumer hardware.

Read on r/LocalLLaMA →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

0.9B model transcribes and diarizes 4-person meeting on MacBook in 12 mins

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0 / 100
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Tool
User-reported performance of a specific model on consumer hardware.
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product, infra
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1 days old
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COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/Binary_orchid ·

    Ran a 0.9B transcribe + diarize model on a 17.5 minute, 4 person AMI meeting on a MacBook: all 4 speakers found, 0.08% speaker confusion, done in 12 minutes

    <!-- SC_OFF --><div class="md"><p>Wanted to see how one model that does transcription, speaker labels and timestamps in a single pass handles a real meeting, so I ran MOSS-Transcribe-Diarize 0.9B on a four person meeting from the AMI test set and scored its speaker turns against …

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