Introducing Muse Voice Transcribe, the first real-time audio perception model from Meta Superintelligence Labs.
Muse Voice Transcribe delivers real-time streaming ASR, diarization with 20+ speakers, and endpointing. It’s multilingual with seamless code-switching and improves accuracy with language, keyword, and context biasing.
The model ranks first on @ArtificialAnlys streaming speech-to-text and on public diarization benchmarks.
Muse Voice Transcribe is an autoregressive multimodal LLM from the Muse Spark family.
Audio is processed in 80ms chunks (12.5 Hz), one token each, and at every chunk the model decides whether to keep listening or emit text.
RL with combined word error rate and delay rewards gives it adaptive delay: it waits longer on hard words and commits sooner on easy ones, trading accuracy against latency word-by-word.
streaming — Sending a model's response token by token as it is generated, so the reader sees text immediately instead of waiting for the whole answer.
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
Why it matters
streamingSending a model's response token by token as it is generated, so the reader sees text immediately instead of waiting for the whole answer.Full definition → transcription with per-word adaptive latency, 20+ speaker diarization, and code switching is available through the Meta Model API, giving voice AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → builders a tunable speed versus accuracy trade-off.
