Meta has launched Muse Voice Transcribe, a new real-time speech-to-text model integrating transcription, endpoint detection, and speaker diarization for over 20 participants. Developed by Meta Superintelligence Labs, Muse processes speech as it happens and supports long audio, multilingual code-switching, and language biasing. While its 20-plus speaker capability is not a world record, it competes strongly by combining high-capacity diarization with low-latency transcription and aggressive pricing. The model was trained on over 70 languages, with 25 validated for initial release. Diarization, which identifies who spoke, is crucial for accurate downstream AI systems and is directly incorporated into Muse's architecture. Muse arrives at a public API price of $0.18 per hour, making it competitive, especially considering diarization is included. Meta's benchmarks show Muse leading in word error rate and demonstrating a lower diarization error rate than some competitors. Although not offering word-level timestamps or confidence scores, Muse presents a strong price-performance proposition for enterprise developers. This launch pressures competitors to focus on speaker-aware accuracy and total cost, not just raw speech recognition.