Meta Superintelligence Labs Releases Muse Voice Transcribe: One Model for Streaming ASR, Diarization, and Endpointing

Meta's Superintelligence Labs has released Muse Voice Transcribe, a single real-time model that handles streaming automatic speech recognition, speaker diarization, and endpointing in a unified architecture rather than chaining separate specialist models. Traditional voice pipelines require separate systems for transcription, speaker identification, and detecting when a speaker has finished — Muse Voice Transcribe collapses all three into one inference pass, reducing latency and system complexity. The model is designed for low-latency streaming use cases such as live captioning, voice agents, and real-time meeting transcription. Developers building voice-enabled agents or conversational interfaces will find this especially relevant as it removes a significant integration burden and latency penalty from multi-model pipeline architectures. This release is part of Meta's broader Muse model family push under the Superintelligence Labs brand.