Meta Superintelligence Labs Releases Muse Voice Transcribe: One Real-Time Model for Streaming ASR, Diarization, and Endpointing
Most production voice stacks are three systems stitched together. One model transcribes, a second separates speakers, and a detector decides when the user stopped talking. Each hand-off adds latency and a new failure mode. Muse Voice Transcribe, announced by Meta Superintelligence Labs this week, collapses those three jobs into a single autoregressive model. Meta calls it its first real-time audio perception model. It performs streaming ASR, speaker diarization for 20+ speakers, and endpoint
Most voice transcription systems require three separate models working in sequence: one to transcribe speech to text, another to identify which speaker is talking, and a third to detect when someone stops speaking. Each handoff between these systems adds delays and potential errors. Meta Superintelligence Labs released a single model that combines all three tasks into one, performing real-time transcription, speaker identification for over 20 speakers, and speech detection simultaneously without needing additional processing steps. The model uses reinforcement learning to adaptively balance accuracy against speed on a word-by-word basis, and it supports over 70 languages with native code-switching for bilingual speakers. It is available only as a paid API service at $0.18 per audio hour, with no publicly released weights for self-hosted use.
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