Meta Superintelligence Labs has released Muse Voice Transcribe, integrating real-time speech recognition, speaker separation, and utterance end detection into a single model, natively supporting over 1 hour of audio and conversations with more than 20 speakers without additional post-processing.

In the English streaming transcription benchmark by Artificial Analysis, Muse Voice Transcribe achieved a final text word error rate of 3.1%, ranking first; GPT Live Transcribe had 3.9%, and Gemini 3.5 Transcribe Live had 4.0%. A lower word error rate indicates that the model's output has a smaller word-level difference from the human reference text.

Muse outputs the final text about 0.16 seconds after the user stops speaking. Traditional real-time transcription often requires a preset fixed waiting window; a shorter window is faster but may truncate sentences, while a longer window is accurate but increases delay. Muse uses 80-millisecond audio chunks as input, allowing the model to autonomously decide whether to continue listening or end the current utterance and submit the transcription result.

Meta claims it optimizes transcription accuracy and endpoint detection delay simultaneously through reinforcement learning. Simple, contextually clear words can be confirmed earlier; ambiguous words that require subsequent audio to distinguish will have more listening time retained. This changes endpoint detection from fixed rules to dynamically allocating computation and waiting budgets based on speech content.

The model training covers over 70 languages, with 25 of them validated at launch, including Mandarin, English, Cantonese, Japanese, Korean, Spanish, Arabic, Hindi, Thai, Vietnamese, and Portuguese. Muse also natively supports code-switching, recognizing mixed languages such as Chinese and English within or between sentences.

Muse is integrated with Meta Model API, Meta AI for Mac, and Muse Code. The API model is muse-voice-transcribe-1.0, priced at $3 per 1,000 audio minutes, or $0.18 per hour; it is currently a managed API service, and Meta has not opened the model weights.

In the market mechanism, buyers include enterprises and developers building meeting notes, real-time customer service, voice agents, interview transcriptions, and multilingual content tools; sellers include voice infrastructure providers such as Meta, OpenAI, Google, ElevenLabs, Deepgram, AssemblyAI, and Cartesia. Funding will flow from manual transcription, post-processing labeling, and multi-model stitching pipelines to low-latency voice models, API calls, meeting intelligence, customer service automation, and voice agents; platforms that can deliver accurate transcription, speaker identity, and real-time endpoint detection will benefit, while providers relying on manual shorthand or high-latency batch processing will face pressure.

Source: Public Information