Optimized implementation for mobile devices
Kyutai's MLX implementation, developed especially for Apple devices, demonstrates excellent mobile adaptability. Based on the hardware acceleration of Apple's MLX framework, the 1B-parameter STT model was able to achieve full real-time speech transcription on the iPhone 16 Pro, with processing latency controlled to less than 1 second. This is attributed to the fact that the model weights are quantized into a 4-bit format while utilizing the Apple Neural Engine (ANE) for matrix operation acceleration.
The mobile implementation offers two modes of operation: offline mode runs entirely on the device side to protect user privacy; online mode connects to a larger 2.6B model in the cloud for higher accuracy. Test data shows that on the M2 chip MacBook Pro, the MLX version is more than 3 times faster than the native PyTorch implementation, with a power consumption reduction of 70%.
The development kit provides a clean Python interface and includes functional modules for real-time microphone capture, audio file processing, and continuous dictation. These features make Kyutai one of the few open source solutions available today that enables professional-grade speech recognition on mobile devices.
This answer comes from the articleKyutai: Speech to text real-time conversion toolThe
